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Berardi M, Brosch K, Pfarr JK, Schneider K, Sültmann A, Thomas-Odenthal F, Wroblewski A, Usemann P, Philipsen A, Dannlowski U, Nenadić I, Kircher T, Krug A, Stein F, Dietrich M. Relative importance of speech and voice features in the classification of schizophrenia and depression. Transl Psychiatry 2023; 13:298. [PMID: 37726285 PMCID: PMC10509176 DOI: 10.1038/s41398-023-02594-0] [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: 02/03/2023] [Revised: 08/10/2023] [Accepted: 09/08/2023] [Indexed: 09/21/2023] Open
Abstract
Speech is a promising biomarker for schizophrenia spectrum disorder (SSD) and major depressive disorder (MDD). This proof of principle study investigates previously studied speech acoustics in combination with a novel application of voice pathology features as objective and reproducible classifiers for depression, schizophrenia, and healthy controls (HC). Speech and voice features for classification were calculated from recordings of picture descriptions from 240 speech samples (20 participants with SSD, 20 with MDD, and 20 HC each with 4 samples). Binary classification support vector machine (SVM) models classified the disorder groups and HC. For each feature, the permutation feature importance was calculated, and the top 25% most important features were used to compare differences between the disorder groups and HC including correlations between the important features and symptom severity scores. Multiple kernels for SVM were tested and the pairwise models with the best performing kernel (3-degree polynomial) were highly accurate for each classification: 0.947 for HC vs. SSD, 0.920 for HC vs. MDD, and 0.932 for SSD vs. MDD. The relatively most important features were measures of articulation coordination, number of pauses per minute, and speech variability. There were moderate correlations between important features and positive symptoms for SSD. The important features suggest that speech characteristics relating to psychomotor slowing, alogia, and flat affect differ between HC, SSD, and MDD.
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Affiliation(s)
- Mark Berardi
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany.
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Katharina Schneider
- Institute for Linguistics: General Linguistics, University of Mainz, Mainz, Germany
| | - Angela Sültmann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Alexandra Philipsen
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Maria Dietrich
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
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Schneider K, Leinweber K, Jamalabadi H, Teutenberg L, Brosch K, Pfarr JK, Thomas-Odenthal F, Usemann P, Wroblewski A, Straube B, Alexander N, Nenadić I, Jansen A, Krug A, Dannlowski U, Kircher T, Nagels A, Stein F. Syntactic complexity and diversity of spontaneous speech production in schizophrenia spectrum and major depressive disorders. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:35. [PMID: 37248240 DOI: 10.1038/s41537-023-00359-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/25/2023] [Indexed: 05/31/2023]
Abstract
Syntax, the grammatical structure of sentences, is a fundamental aspect of language. It remains debated whether reduced syntactic complexity is unique to schizophrenia spectrum disorder (SSD) or whether it is also present in major depressive disorder (MDD). Furthermore, the association of syntax (including syntactic complexity and diversity) with language-related neuropsychology and psychopathological symptoms across disorders remains unclear. Thirty-four SSD patients and thirty-eight MDD patients diagnosed according to DSM-IV-TR as well as forty healthy controls (HC) were included and tasked with describing four pictures from the Thematic Apperception Test. We analyzed the produced speech regarding its syntax delineating measures for syntactic complexity (the total number of main clauses embedding subordinate clauses) and diversity (number of different types of complex sentences). We performed cluster analysis to identify clusters based on syntax and investigated associations of syntactic, to language-related neuropsychological (verbal fluency and verbal episodic memory), and psychopathological measures (positive and negative formal thought disorder) using network analyses. Syntax in SSD was significantly reduced in comparison to MDD and HC, whereas the comparison of HC and MDD revealed no significant differences. No associations were present between speech measures and current medication, duration and severity of illness, age or sex; the single association accounted for was education. A cluster analysis resulted in four clusters with different degrees of syntax across diagnoses. Subjects with less syntax exhibited pronounced positive and negative symptoms and displayed poorer performance in executive functioning, global functioning, and verbal episodic memory. All cluster-based networks indicated varying degrees of domain-specific and cross-domain connections. Measures of syntactic complexity were closely related while syntactic diversity appeared to be a separate node outside of the syntactic network. Cross-domain associations were more salient in more complex syntactic production.
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Affiliation(s)
- Katharina Schneider
- Department of English and Linguistics, General Linguistics, University of Mainz, Mainz, Germany.
| | - Katrin Leinweber
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Arne Nagels
- Department of English and Linguistics, General Linguistics, University of Mainz, Mainz, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
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Choi G, Kim W, Koo J. Investigating the Performance of Machine Learning Methods in Predicting Functional Properties of the Hydrogenase Variants. BIOTECHNOL BIOPROC E 2023. [DOI: 10.1007/s12257-022-0330-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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Krämer C, Stumme J, da Costa Campos L, Rubbert C, Caspers J, Caspers S, Jockwitz C. Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach. Netw Neurosci 2023; 7:122-147. [PMID: 37339286 PMCID: PMC10270720 DOI: 10.1162/netn_a_00275] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/22/2022] [Indexed: 09/22/2023] Open
Abstract
Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.
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Affiliation(s)
- Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lucas da Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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A comparative study of retinal layer changes among patients with schizophrenia and healthy controls. Acta Neuropsychiatr 2022; 35:165-176. [PMID: 36476516 DOI: 10.1017/neu.2022.35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AIM This study aimed to evaluate the retinal nerve fibre layer changes among different group of patients with schizophrenia and compare it with healthy controls by using swept-source optical coherence tomography. METHODOLOGY Patients with first-episode schizophrenia (n = 21) in remission (n = 35) or with treatment-resistant schizophrenia (TRS) (n = 35) and 36 healthy controls were evaluated for retinal thickness. RESULTS Patients with psychotic illnesses had significantly lower sub-foveal choroidal thickness (effect size 0.84-0.86), when compared to the healthy controls. When patients with first-episode schizophrenia were compared with patients with TRS, TRS patients had significant lower sub-foveal choroidal thickness (left eye) when the various confounders (such as age, gender, duration of treatment, smoking, current medications, body mass index, waist circumference, blood pressure, fasting glucose, HbA1c, presence or absence of metabolic syndrome) were taken into account. When the patients with TRS were compared with healthy controls, initially significant differences were observed for the macular volume (left and right) and the ganglion cell thickness (right eye) but these differences disappeared after controlling for the various covariates. CONCLUSIONS Compared to healthy controls, patients with schizophrenia, psychotic illnesses have thinning of the retina, especially in the sub-foveal choroidal thickness.
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Analysis of color vision and cognitive function in first-episode schizophrenia before and after antipsychotic treatment. J Psychiatr Res 2022; 152:278-288. [PMID: 35759980 DOI: 10.1016/j.jpsychires.2022.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND A large body of recent research has demonstrated that patients with schizophrenia exhibit significant changes in visual function and ocular tissue structure in the early stages of onset. It is therefore possible to explore a novel scientific breakthrough in the etiology of schizophrenia by transforming the traditional study of brain structure and function with a view to examining the potential field of eye tissue and function. However, few studies have investigated the correlation between iris characteristics and schizophrenia, and evidence is lacking in this regard. Thus, further exploration is needed. PURPOSE This study was designed to analyze the characteristics of iris structure, color vision function and cognitive function, as well as the changes therein in patients with the first-episode drug-free schizophrenia before and after antipsychotic treatment. It aimed to preliminarily identify easily-measurable biomarkers for early clinical screening and diagnosis of schizophrenia. METHODS This study recruited 61 patients (22 males) with first-episode schizophrenia. Prior to the commencement of treatment with antipsychotic drugs, the Montreal Cognitive Assessment (MoCA) and Farnsworth-Munsell Dichotomous (D-15 Hue Test) were used as assessment tools to evaluate cognitive function and color vision function, respectively. Over a 6-week period, patients received a second-generation antipsychotic treatment (all converted to olanzapine equivalent dose) as prescribed by the doctor, and the Positive and Negative Syndrome Scale (PANSS) was applied to evaluate the clinical treatment effects before treatment (baseline), as well as at the 2nd, 4th, and 6th weeks after drug treatment. On the basis of iris characteristics, the patients were divided into groups. The observed differences in drug treatment effects between the groups were then compared and analyzed to further clarify the relationship between treatment efficacy and iris characteristics. Finally, changes in the cognitive function and color vision function of patients at baseline and at the 6th week after drug treatment were compared, and the effects of antipsychotic drug treatment on the above-mentioned functions were analyzed. RESULTS On the basis of structural iris characteristics, 61 patients were classified as follows: 28 patients without iris crypts and 33 with iris crypts; 35 without iris pigment dots and 26 with iris pigment dots; 42 without iris wrinkles and 19 with iris wrinkles. No significant difference was observed in the PANSS scores of all of the patients at baseline; however, significant differences were found in patients with iris crypts and iris pigment dots at each follow-up timepoint (i.e., at the 2nd, 4th, and 6th week). Moreover, it is noteworthy that, compared with other patients, the PANSS scores of patients without specific iris structure characteristics (iris crypts and pigment dots) decreased significantly (P<0.05), which indicated that the drug therapy was highly effective. Excluding the interference of drug factors, a significant correlation was found between the results of the D-15 (color vision function) and MoCA (cognitive function) in first-episode untreated patients (r = -0.401, P < 0.05). In addition, the MoCA scores (mean difference = 2.36, t = 10.05, P ˂ 0.01) were significantly higher after 6 weeks of antipsychotic drug treatment compared to conditions at baseline. CONCLUSIONS The findings of this study demonstrated that color vision function of patients with schizophrenia improved with the improvement of cognitive function. The structural characteristics of the iris with crypts and pigment dots could have a significant impact on the drug treatment effect of schizophrenia and could be considered as a potential biomarker for detecting and recognizing schizophrenia.
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Wang T, Bezerianos A, Cichocki A, Li J. Multikernel Capsule Network for Schizophrenia Identification. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:4741-4750. [PMID: 33259321 DOI: 10.1109/tcyb.2020.3035282] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine-learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multikernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match partition sizes of the brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of the widely used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized the capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multikernel capsule structure with the consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification.
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Rao B, Cheng H, Xu H, Peng Y. Random Network and Non-rich-club Organization Tendency in Children With Non-syndromic Cleft Lip and Palate After Articulation Rehabilitation: A Diffusion Study. Front Neurol 2022; 13:790607. [PMID: 35185761 PMCID: PMC8847279 DOI: 10.3389/fneur.2022.790607] [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: 10/07/2021] [Accepted: 01/03/2022] [Indexed: 11/30/2022] Open
Abstract
Objective The neuroimaging pattern in brain networks after articulation rehabilitation can be detected using graph theory and multivariate pattern analysis (MVPA). In this study, we hypothesized that the characteristics of the topology pattern of brain structural network in articulation-rehabilitated children with non-syndromic cleft lip and palate (NSCLP) were similar to that in healthy comparisons. Methods A total of 28 children with NSCLP and 28 controls with typical development were scanned for diffusion tensor imaging on a 3T MRI scanner. Structural networks were constructed, and their topological properties were obtained. Besides, the Chinese language clear degree scale (CLCDS) scores were used for correlation analysis with topological features in patients with NSCLP. Results The NSCLP group showed a similar rich-club connection pattern, but decreased small-world index, normalized rich-club coefficient, and increased connectivity strength of connections compared to controls. The univariate and multivariate patterns of the structural network in articulation-rehabilitated children were primarily in the feeder and local connections, covering sensorimotor, visual, frontoparietal, default mode, salience, and language networks, and orbitofrontal cortex. In addition, the connections that were significantly correlated with the CLCDS scores, as well as the weighted regions for classification, were chiefly distributed in the dorsal and ventral stream associated with the language networks of the non-dominant hemisphere. Conclusion The average level rich-club connection pattern and the compensatory of the feeder and local connections mainly covering language networks may be related to the CLCDS in articulation-rehabilitated children with NSCLP. However, the patterns of small-world and rich-club structural organization in the articulation-rehabilitated children exhibited a random network and non-rich-club organization tendency. These findings enhanced the understanding of neuroimaging patterns in children with NSCLP after articulation rehabilitation.
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Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
| | - Hua Cheng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
- *Correspondence: Haibo Xu
| | - Yun Peng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
- Yun Peng
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Abdelrahman HAF, Ubukata S, Ueda K, Fujimoto G, Oishi N, Aso T, Murai T. Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects. Neuropsychiatr Dis Treat 2022; 18:1801-1814. [PMID: 36039160 PMCID: PMC9419894 DOI: 10.2147/ndt.s354265] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 07/01/2022] [Indexed: 11/23/2022] Open
Abstract
AIM Diffuse axonal injury (DAI) is one of the most common pathological features of traumatic brain injury (TBI). Diffusion tensor imaging (DTI) indices can be used to identify and quantify white matter microstructural changes following DAI. Recently, many studies have used DTI with various machine learning approaches to predict white matter microstructural changes following TBI. The current study sought to examine whether our classification approach using multiple DTI indices in conjunction with machine learning is a useful tool for diagnosing/classifying TBI patients and healthy controls. METHODS Participants were adult patients with chronic TBI (n = 26) with DAI pathology, and age- and sex-matched healthy controls (n = 26). DTI images were obtained from all participants. Tract-based spatial statistics analyses were applied to DTI images. Classification models were built using principal component analysis and support vector machines. Receiver operator characteristic curve analysis and area under the curve were used to assess the classification performance of the different classifiers. RESULTS Tract-based spatial statistics revealed significantly decreased fractional anisotropy, as well as increased mean diffusivity, axial diffusivity, and radial diffusivity in patients with TBI compared with healthy controls (all p-values < 0.01). The principal component analysis and support vector machine-based machine learning classification using combined DTI indices classified patients with TBI and healthy controls with an accuracy of 90.5% with an area under the curve of 93 ± 0.09. CONCLUSION These results highlight the potential of our approach combining multiple DTI measures to identify patients with TBI.
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Affiliation(s)
| | - Shiho Ubukata
- Kyoto University Graduate School of Medicine-Medical Innovation Center, Kyoto, 606-8507, Japan
| | - Keita Ueda
- Kyoto University Graduate School of Medicine-Department of Psychiatry, Kyoto, 606-8507, Japan
| | - Gaku Fujimoto
- Kyoto University Graduate School of Medicine-Department of Psychiatry, Kyoto, 606-8507, Japan
| | - Naoya Oishi
- Kyoto University Graduate School of Medicine-Medical Innovation Center, Kyoto, 606-8507, Japan
| | - Toshihiko Aso
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, 650-0047, Japan
| | - Toshiya Murai
- Kyoto University Graduate School of Medicine-Department of Psychiatry, Kyoto, 606-8507, Japan
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Solanes A, Radua J. Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There? Front Psychiatry 2022; 13:fpsyt-13-826111. [PMID: 35492715 PMCID: PMC9039205 DOI: 10.3389/fpsyt.2022.826111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Aleix Solanes
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Department of Psychiatry and Forensic Medicine, School of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Early Psychosis: Interventions and Clinical-detection Lab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Clinical Neuroscience, Stockholm Health Care Services, Stockholm County Council, Karolinska Institutet, Stockholm, Sweden
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Hu K, Wang M, Liu Y, Yan H, Song M, Chen J, Chen Y, Wang H, Guo H, Wan P, Lv L, Yang Y, Li P, Lu L, Yan J, Wang H, Zhang H, Zhang D, Wu H, Ning Y, Jiang T, Liu B. Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score. Neuroimage Clin 2021; 32:102860. [PMID: 34749286 PMCID: PMC8567302 DOI: 10.1016/j.nicl.2021.102860] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 09/29/2021] [Accepted: 10/13/2021] [Indexed: 11/21/2022]
Abstract
Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.
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Affiliation(s)
- Ke Hu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Meng Wang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hao Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Peng Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Lin Lu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Jun Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Huiling Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China; Department of Psychology, Xinxiang Medical University, Xinxiang, China
| | - Dai Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China; Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Queensland Brain Institute, University of Queensland, Brisbane, Australia.
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China.
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12
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Haas SS, Antonucci LA, Wenzel J, Ruef A, Biagianti B, Paolini M, Rauchmann BS, Weiske J, Kambeitz J, Borgwardt S, Brambilla P, Meisenzahl E, Salokangas RKR, Upthegrove R, Wood SJ, Koutsouleris N, Kambeitz-Ilankovic L. A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis. Neuropsychopharmacology 2021; 46:828-835. [PMID: 33027802 PMCID: PMC8027389 DOI: 10.1038/s41386-020-00877-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 09/11/2020] [Accepted: 09/15/2020] [Indexed: 02/07/2023]
Abstract
Two decades of studies suggest that computerized cognitive training (CCT) has an effect on cognitive improvement and the restoration of brain activity. Nevertheless, individual response to CCT remains heterogenous, and the predictive potential of neuroimaging in gauging response to CCT remains unknown. We employed multivariate pattern analysis (MVPA) on whole-brain resting-state functional connectivity (rsFC) to (neuro)monitor clinical outcome defined as psychosis-likeness change after 10-hours of CCT in recent onset psychosis (ROP) patients. Additionally, we investigated if sensory processing (SP) change during CCT is associated with individual psychosis-likeness change and cognitive gains after CCT. 26 ROP patients were divided into maintainers and improvers based on their SP change during CCT. A support vector machine (SVM) classifier separating 56 healthy controls (HC) from 35 ROP patients using rsFC (balanced accuracy of 65.5%, P < 0.01) was built in an independent sample to create a naturalistic model representing the HC-ROP hyperplane. This model was out-of-sample cross-validated in the ROP patients from the CCT trial to assess associations between rsFC pattern change, cognitive gains and SP during CCT. Patients with intact SP threshold at baseline showed improved attention despite psychosis status on the SVM hyperplane at follow-up (p < 0.05). Contrarily, the attentional gains occurred in the ROP patients who showed impaired SP at baseline only if rsfMRI diagnosis status shifted to the healthy-like side of the SVM continuum. Our results reveal the utility of MVPA for elucidating treatment response neuromarkers based on rsFC-SP change and pave the road to more personalized interventions.
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Affiliation(s)
- Shalaila S. Haas
- grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Linda A. Antonucci
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany ,grid.7644.10000 0001 0120 3326Department of Education, Psychology, Communication – University of Bari “Aldo Moro”, Bari, Italy
| | - Julian Wenzel
- grid.6190.e0000 0000 8580 3777University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Anne Ruef
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Bruno Biagianti
- grid.438587.50000 0004 0450 1574Department of R&D, Posit Science Corporation, San Francisco, CA USA ,grid.4708.b0000 0004 1757 2822Department of Pathophysiology and Transplantation, Faculty of Medicine and Surgery, University of Milan, Milan, Italy
| | - Marco Paolini
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Boris-Stephan Rauchmann
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany ,Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Johanna Weiske
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Joseph Kambeitz
- grid.6190.e0000 0000 8580 3777University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Stefan Borgwardt
- grid.4562.50000 0001 0057 2672Translational Psychiatry Unit (TPU), Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
| | - Paolo Brambilla
- grid.414818.00000 0004 1757 8749Department of Neuroscience and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy ,grid.4708.b0000 0004 1757 2822Department of Pathophysiology and Mental Health, University of Milan, Milan, Italy
| | - Eva Meisenzahl
- grid.411327.20000 0001 2176 9917Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Raimo K. R. Salokangas
- grid.1374.10000 0001 2097 1371Department of Psychiatry, University of Turku, Turku, Finland
| | - Rachel Upthegrove
- grid.6572.60000 0004 1936 7486School of Psychology, University of Birmingham, Birmingham, United Kingdom ,grid.6572.60000 0004 1936 7486Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Stephen J. Wood
- grid.6572.60000 0004 1936 7486School of Psychology, University of Birmingham, Birmingham, United Kingdom ,grid.488501.0Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XCentre for Youth Mental Health, University of Melbourne, Melbourne, VIC Australia
| | - Nikolaos Koutsouleris
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany. .,University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany.
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Todeva-Radneva A, Paunova R, Kandilarova S, St Stoyanov D. The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review. Curr Top Med Chem 2021; 20:540-553. [PMID: 32003690 DOI: 10.2174/1568026620666200131095328] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 01/05/2023]
Abstract
Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.
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Affiliation(s)
- Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy St Stoyanov
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
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Yamamoto M, Bagarinao E, Kushima I, Takahashi T, Sasabayashi D, Inada T, Suzuki M, Iidaka T, Ozaki N. Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites. PLoS One 2020; 15:e0239615. [PMID: 33232334 PMCID: PMC7685428 DOI: 10.1371/journal.pone.0239615] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 09/10/2020] [Indexed: 12/17/2022] Open
Abstract
Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.
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Affiliation(s)
- Maeri Yamamoto
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, Nagoya, Aichi, Japan
| | | | - Itaru Kushima
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, Nagoya, Aichi, Japan
- Medical Genomics Center, Nagoya University Hospital, Nagoya, Aichi, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Toyama, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Toyama, Japan
| | - Toshiya Inada
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Toyama, Japan
| | - Tetsuya Iidaka
- Brain & Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- * E-mail:
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, Nagoya, Aichi, Japan
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An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study. Pain Ther 2020; 9:601-614. [PMID: 32880867 PMCID: PMC7648771 DOI: 10.1007/s40122-020-00191-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Chronic pain (CP) is a complex multidimensional experience severely affecting individuals' quality of life. Multiple cognitive, affective, emotional, and interpersonal factors play a major role in CP. Furthermore, the psychological, social, and physical circumstances leading to CP show high inter-individual variability, thus making it difficult to identify core syndrome characteristics. In a biopsychosocial perspective, we aim at identifying a pattern of psycho-physical impairments that can reliably discriminate between CP individuals and healthy controls (HC) with high accuracy and estimated generalizability using machine learning. METHODS A total of 118 CP and 86 HC were recruited. All individuals were administered several scales assessing quality of life, physical and mental health, personal functioning, anxiety, depression, beliefs about medical treatments, and cognitive ability. These features were trained to separate CP from HC using support vector classification and repeated nested cross-validation. RESULTS Our psycho-physical classifier was able to discriminate CP from HC with 86.5% balanced accuracy and significance (p = 0.0001). The most reliable features characterizing CP were anxiety and depression scores, and belief of harm from prolonged pharmacological treatments; for HP, the most reliable features were physical and occupational functioning, and vitality levels. CONCLUSION Our findings suggest that, using psychological and physical assessments, it is possible to classify CP from HC with high reliability and estimated generalizability via (i) a pattern of psychological symptoms and cognitive beliefs characteristic of CP, and (ii) a pattern of intact physical functioning characteristic of HC. We think that our algorithm enables novel insights into potential individualized targets for CP-related early intervention programs.
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Di Carlo P, Pergola G, Antonucci LA, Bonvino A, Mancini M, Quarto T, Rampino A, Popolizio T, Bertolino A, Blasi G. Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals. Psychol Med 2020; 50:1501-1509. [PMID: 31358071 DOI: 10.1017/s0033291719001430] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case-control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities. METHODS Following a previous report, we used Random Forests to predict diagnosis in a case-control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire. RESULTS Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features. CONCLUSIONS These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease.
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Affiliation(s)
- Pasquale Di Carlo
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus - Baltimore, MD, USA
| | - Giulio Pergola
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus - Baltimore, MD, USA
| | - Linda A Antonucci
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
- Department of Psychiatry and Psychotherapy - Ludwig-Maximilians University, Munich, Germany
| | - Aurora Bonvino
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
- IRCCS 'Casa Sollievo della Sofferenza', San Giovanni Rotondo, Italy
| | - Marina Mancini
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
| | - Tiziana Quarto
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
| | - Antonio Rampino
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Teresa Popolizio
- IRCCS 'Casa Sollievo della Sofferenza', San Giovanni Rotondo, Italy
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
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17
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Burkhardt G, Adorjan K, Kambeitz J, Kambeitz-Ilankovic L, Falkai P, Eyer F, Koller G, Pogarell O, Koutsouleris N, Dwyer DB. A machine learning approach to risk assessment for alcohol withdrawal syndrome. Eur Neuropsychopharmacol 2020; 35:61-70. [PMID: 32418843 DOI: 10.1016/j.euroneuro.2020.03.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 03/04/2020] [Accepted: 03/27/2020] [Indexed: 02/01/2023]
Abstract
At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to predict alcohol withdrawal outcomes at the individual subject level using information easily attainable at patients' admission. An observational machine learning analysis using nested cross-validation and out-of-sample validation was applied to alcohol-dependent patients at two major detoxification wards (LMU, n = 389; TU, n = 805). 121 retrospectively derived clinical, blood-derived, and sociodemographic measures were used to predict 1) moderate to severe withdrawal defined by the alcohol withdrawal scale, 2) delirium tremens, and 3) withdrawal seizures. Mild and more severe withdrawal cases could be separated with significant, although highly variable accuracy in both samples (LMU, balanced accuracy [BAC] = 69.4%; TU, BAC = 55.9%). Poor outcome predictions were associated with higher cumulative clomethiazole doses during the withdrawal course. Delirium tremens was predicted in the TU cohort with BAC of 75%. No significant model predicting withdrawal seizures could be found. Our models were unique to each treatment site and thus did not generalize. For both treatment sites and withdrawal outcome different variable sets informed our models' decisions. Besides previously described variables (most notably, thrombocytopenia), we identified new predictors (history of blood pressure abnormalities, urine screening for benzodiazepines and educational attainment). In conclusion, machine learning approaches may facilitate generalizable, individualized predictions for alcohol withdrawal severity. Since predictive patterns highly vary for different outcomes of withdrawal severity and across treatment sites, prediction tools should not be recommended for clinical practice unless adequately validated in specific cohorts.
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Affiliation(s)
- Gerrit Burkhardt
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany.
| | - Kristina Adorjan
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany; Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, University of Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany
| | - Florian Eyer
- Department of Clinical Toxicology, TUM School of Medicine, Technical University of Munich, Germany
| | - Gabi Koller
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany
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Du Y, Hao H, Wang S, Pearlson GD, Calhoun VD. Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis. Neuroimage Clin 2020; 27:102284. [PMID: 32563920 PMCID: PMC7306624 DOI: 10.1016/j.nicl.2020.102284] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/29/2020] [Accepted: 05/19/2020] [Indexed: 12/23/2022]
Abstract
It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.
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Affiliation(s)
- Yuhui Du
- School of Computer & Information Technology, Shanxi University, Taiyuan, China; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
| | - Hui Hao
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Shuhua Wang
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | | | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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Abstract
PURPOSE OF REVIEW After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established. This article reviews current advances in evaluating the use of language as a diagnostic or prognostic tool in schizophrenia. RECENT FINDINGS The development of computational linguistic tools to quantify language disturbances is rapidly gaining ground in the field of schizophrenia research. Current applications are the use of semantic space models and acoustic analyses focused on phonetic markers. These features are used in machine learning models to distinguish patients with schizophrenia from healthy controls or to predict conversion to psychosis in high-risk groups, reaching accuracy scores (generally ranging from 80 to 90%) that exceed clinical raters. Other potential applications for a language biomarker in schizophrenia are monitoring of side effects, differential diagnostics and relapse prevention. SUMMARY Language disturbances are a key feature of schizophrenia. Although in its early stages, the emerging field of research focused on computational linguistics suggests an important role for language analyses in the diagnosis and prognosis of schizophrenia. Spoken language as a biomarker for schizophrenia has important advantages because it can be objectively and reproducibly quantified. Furthermore, language analyses are low-cost, time efficient and noninvasive in nature.
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Language in schizophrenia: relation with diagnosis, symptomatology and white matter tracts. NPJ SCHIZOPHRENIA 2020; 6:10. [PMID: 32313047 PMCID: PMC7171150 DOI: 10.1038/s41537-020-0099-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 02/28/2020] [Indexed: 01/04/2023]
Abstract
Language deviations are a core symptom of schizophrenia. With the advances in computational linguistics, language can be easily assessed in exact and reproducible measures. This study investigated how language characteristics relate to schizophrenia diagnosis, symptom, severity and integrity of the white matter language tracts in patients with schizophrenia and healthy controls. Spontaneous speech was recorded and diffusion tensor imaging was performed in 26 schizophrenia patients and 22 controls. We were able to classify both groups with a sensitivity of 89% and a specificity of 82%, based on mean length of utterance and clauses per utterance. Language disturbances were associated with negative symptom severity. Computational language measures predicted language tract integrity in patients (adjusted R2 = 0.467) and controls (adjusted R2 = 0.483). Quantitative language analyses have both clinical and biological validity, offer a simple, helpful marker of both severity and underlying pathology, and provide a promising tool for schizophrenia research and clinical practice.
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21
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Machine learning technique reveals intrinsic characteristics of schizophrenia: an alternative method. Brain Imaging Behav 2020; 13:1386-1396. [PMID: 30159765 DOI: 10.1007/s11682-018-9947-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Machine learning technique has long been utilized to assist disease diagnosis, increasing clinical physicians' confidence in their decision and expediting the process of diagnosis. In this case, machine learning technique serves as a tool for distinguishing patients from healthy people. Additionally, it can also serve as an exploratory method to reveal intrinsic characteristics of a disease based on discriminative features, which was demonstrated in this study. Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 148 participants (including patients with schizophrenia and healthy controls). Connective strengths were estimated by Pearson correlation for each pair of brain regions partitioned according to automated anatomical labelling atlas. Subsequently, consensus connections with high discriminative power were extracted under the circumstance of the best classification accuracy. Investigating these consensus connections, we found that schizophrenia group predominately exhibited weaker strengths of inter-regional connectivity compared to healthy group. Aberrant connectivities in both intra- and inter-hemispherical connections were observed. Within intra-hemispherical connections, the number of aberrant connections in the right hemisphere was more than that of the left hemisphere. In the exploration of large regions, we revealed that the serious dysconnectivities mainly appeared on temporal and occipital regions for the within-large-region connections; while connectivity disruption was observed on the connections from temporal region to occipital, insula and limbic regions for the between-large-region connections. The findings of this study corroborate previous conclusion of dysconnectivity in schizophrenia and further shed light on distribution patterns of dysconnectivity, which deepens the understanding of pathological mechanism of schizophrenia.
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22
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Vieira S, Gong QY, Pinaya WHL, Scarpazza C, Tognin S, Crespo-Facorro B, Tordesillas-Gutierrez D, Ortiz-García V, Setien-Suero E, Scheepers FE, Van Haren NEM, Marques TR, Murray RM, David A, Dazzan P, McGuire P, Mechelli A. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull 2020; 46:17-26. [PMID: 30809667 PMCID: PMC6942152 DOI: 10.1093/schbul/sby189] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.
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Affiliation(s)
- Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Qi-yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, China
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
- Centre of Mathematics, Computation, and Cognition, Universidade Federal do ABC, São Paulo, Brazil
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
- Department of General Psychology, University of Padova, Padova, Italy
| | - Stefania Tognin
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Diana Tordesillas-Gutierrez
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Santander, Cantabria, Spain
| | - Victor Ortiz-García
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Esther Setien-Suero
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Floortje E Scheepers
- Department of Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Neeltje E M Van Haren
- Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Tiago R Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Anthony David
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
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23
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Thomas RM, Bruin W, Zhutovsky P, van Wingen G. Dealing with missing data, small sample sizes, and heterogeneity in machine learning studies of brain disorders. Mach Learn 2020. [DOI: 10.1016/b978-0-12-815739-8.00014-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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24
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Chang YW, Tsai SJ, Wu YF, Yang AC. Development of an Al-Based Web Diagnostic System for Phenotyping Psychiatric Disorders. Front Psychiatry 2020; 11:542394. [PMID: 33250789 PMCID: PMC7674487 DOI: 10.3389/fpsyt.2020.542394] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 09/14/2020] [Indexed: 12/14/2022] Open
Abstract
Background: Artificial intelligence (AI)-based medical diagnostic applications are on the rise. Our recent study has suggested an explainable deep neural network (EDNN) framework for identifying key structural deficits related to the pathology of schizophrenia. Here, we presented an AI-based web diagnostic system for schizophrenia under the EDNN framework with three-dimensional (3D) visualization of subjects' neuroimaging dataset. Methods: This AI-based web diagnostic system consisted of a web server and a neuroimaging diagnostic database. The web server deployed the EDNN algorithm under the Node.js environment. Feature selection and network model building were performed on the dataset obtained from two hundred schizophrenic patients and healthy controls in the Taiwan Aging and Mental Illness (TAMI) cohort. We included an independent cohort with 88 schizophrenic patients and 44 healthy controls recruited at Tri-Service General Hospital Beitou Branch for validation purposes. Results: Our AI-based web diagnostic system achieved 84.00% accuracy (89.47% sensitivity, 80.62% specificity) for gray matter (GM) and 90.22% accuracy (89.21% sensitivity, 91.23% specificity) for white matter (WM) on the TAMI cohort. For the Beitou cohort as an unseen test set, the model achieved 77.27 and 70.45% accuracy for GM and WM. Furthermore, it achieved 85.50 and 88.20% accuracy after model retraining to mitigate the effects of drift on the predictive capability. Moreover, our system visualized the identified voxels in brain atrophy in a 3D manner with patients' structural image, optimizing the evaluation process of the diagnostic results. Discussion: Together, our approach under the EDNN framework demonstrated the potential future direction of making a schizophrenia diagnosis based on structural brain imaging data. Our deep learning model is explainable, arguing for the accuracy of the key information related to the pathology of schizophrenia when using the AI-based web assessment platform. The rationale of this approach is in accordance with the Research Domain Criteria suggested by the National Institute of Mental Health.
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Affiliation(s)
- Yu-Wei Chang
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yung-Fu Wu
- Department of Psychiatry, Beitou Branch, Tri-service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Albert C Yang
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan.,Brain Medicine Center, Tao-Yuan Psychiatric Center, Tao-Yuan, Taiwan
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25
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Winterburn JL, Voineskos AN, Devenyi GA, Plitman E, de la Fuente-Sandoval C, Bhagwat N, Graff-Guerrero A, Knight J, Chakravarty MM. Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study. Schizophr Res 2019; 214:3-10. [PMID: 29274736 DOI: 10.1016/j.schres.2017.11.038] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 11/24/2017] [Accepted: 11/29/2017] [Indexed: 10/18/2022]
Abstract
Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validation techniques. Here, we perform a critical appraisal of the accuracy of machine learning methodologies used in SZ/HC classifications studies by comparing three machine learning algorithms (logistic regression [LR], support vector machines [SVMs], and linear discriminant analysis [LDA]) on three independent datasets (435 subjects total) using two tissue density estimates and cortical thickness (CT). Performance is assessed using 10-fold cross-validation, as well as a held-out validation set. Classification using CT outperformed tissue densities, but there was no clear effect of dataset. LR, SVMs, and LDA each yielded the highest accuracies for a different feature set and validation paradigm, but most accuracies were between 55 and 70%, well below previously reported values. The highest accuracy achieved was 73.5% using CT data and an SVM. Taken together, these results illustrate some of the obstacles to constructing effective disease classifiers, and suggest that tissue densities and CT may not be sufficiently sensitive for SZ/HC classification given current available methodologies and sample sizes.
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Affiliation(s)
- Julie L Winterburn
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Geriatric Mental Health Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Eric Plitman
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Camilo de la Fuente-Sandoval
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico; Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Nikhil Bhagwat
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Ariel Graff-Guerrero
- Geriatric Mental Health Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Jo Knight
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Data Science Institute and Medical School, Lancaster University, Bailrigg, United Kingdom
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
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26
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Zarogianni E, Storkey AJ, Borgwardt S, Smieskova R, Studerus E, Riecher-Rössler A, Lawrie SM. Individualized prediction of psychosis in subjects with an at-risk mental state. Schizophr Res 2019; 214:18-23. [PMID: 28935170 DOI: 10.1016/j.schres.2017.08.061] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 08/28/2017] [Accepted: 08/31/2017] [Indexed: 11/19/2022]
Abstract
Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study. The same neuroanatomical-based pattern classification pipeline, consisting of a linear Support Vector Machine (SVM) and a Recursive Feature Selection (RFE) achieved 74% accuracy in predicting later onset of psychosis. The discriminative neuroanatomical pattern underlying this finding consisted of many brain areas across all four lobes and the cerebellum. These results provide proof-of-concept that the early diagnosis of psychosis is feasible using neuroanatomical-based pattern recognition.
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Affiliation(s)
- Eleni Zarogianni
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK.
| | - Amos J Storkey
- Institute for Adaptive and Neural Computation, University of Edinburgh, UK
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Switzerland
| | - Renata Smieskova
- Department of Psychiatry (UPK), University of Basel, Switzerland
| | - Erich Studerus
- Center for Gender Research and Early Detection, University of Basel Psychiatric Hospital, Switzerland
| | - Anita Riecher-Rössler
- Center for Gender Research and Early Detection, University of Basel Psychiatric Hospital, Switzerland
| | - Stephen M Lawrie
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK
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27
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Talpalaru A, Bhagwat N, Devenyi GA, Lepage M, Chakravarty MM. Identifying schizophrenia subgroups using clustering and supervised learning. Schizophr Res 2019; 214:51-59. [PMID: 31455518 DOI: 10.1016/j.schres.2019.05.044] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 05/28/2019] [Accepted: 05/30/2019] [Indexed: 01/18/2023]
Abstract
Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, asociality), and cognitive symptoms. However, both symptom burden and associated brain alterations are highly heterogeneous and intimately linked to prognosis. In this study, we present a method to predict individual symptom profiles by first deriving clinical subgroups and then using machine learning methods to perform subject-level classification based on magnetic resonance imaging (MRI) derived neuroanatomical measures. Symptomatic and MRI data of 167 subjects were used. Subgroups were defined using hierarchical clustering of clinical data resulting in 3 stable clusters: 1) high symptom burden, 2) predominantly positive symptom burden, and 3) mild symptom burden. Cortical thickness estimates were obtained in 78 regions of interest and were input, along with demographic data, into three machine learning models (logistic regression, support vector machine, and random forest) to predict subgroups. Random forest performance metrics for predicting the group membership of the high and mild symptom burden groups exceeded those of the baseline comparison of the entire schizophrenia population versus normal controls (AUC: 0.81 and 0.78 vs. 0.75). Additionally, an analysis of the most important features in the random forest classification demonstrated consistencies with previous findings of regional impairments and symptoms of schizophrenia.
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Affiliation(s)
- Alexandra Talpalaru
- Biological & Biomedical Engineering, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada; Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada.
| | - Nikhil Bhagwat
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, 27 King's College Cir, Toronto, ON M5S 3H7, Canada
| | - Gabriel A Devenyi
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada
| | - Martin Lepage
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada
| | - M Mallar Chakravarty
- Biological & Biomedical Engineering, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada; Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada.
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28
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Xiao Y, Yan Z, Zhao Y, Tao B, Sun H, Li F, Yao L, Zhang W, Chandan S, Liu J, Gong Q, Sweeney JA, Lui S. Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI. Schizophr Res 2019; 214:11-17. [PMID: 29208422 DOI: 10.1016/j.schres.2017.11.037] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 11/19/2017] [Accepted: 11/27/2017] [Indexed: 02/05/2023]
Abstract
Although regional brain deficits have been demonstrated in schizophrenia patients by structural MRI studies, one important question that remains largely unanswered is whether the complex and subtle deficits revealed by MRI could be used as objective biomarkers to discriminate patients from healthy controls individually. To address this question, a total of 326 right-handed participants were recruited, including 163 drug-naïve first-episode schizophrenia (FES) patients and 163 demographically matched healthy controls. High-resolution anatomic data were acquired from all subjects and processed via Freesurfer software to obtain cortical thickness and surface area measurements. Subsequently, the Support Vector Machine (SVM) was used to explore the potential utility for cortical thickness and surface area measurements in the differentiation of individual patients and healthy controls. The accuracy of correct classification of patients and controls was 85.0% (specificity 87.0%, sensitivity 83.0%) for surface area and 81.8% (specificity 85.0%, sensitivity 76.9%) for cortical thickness (p<0.001 after permutation testing). Regions contributing to classification accuracy mainly included the gray matter in default mode, central executive, salience, and visual networks. Current findings, in a sample of never-treated FES patients, suggest that the patterns of illness-related gray matter changes has potential as a biomarker for identifying structural brain alterations in individuals with schizophrenia. Future prospective studies are needed to evaluate the utility of imaging biomarkers for research and potentially for clinical purpose.
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Affiliation(s)
- Yuan Xiao
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, China
| | - Youjin Zhao
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Bo Tao
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Huaiqiang Sun
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Fei Li
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Li Yao
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Wenjing Zhang
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Shah Chandan
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Jieke Liu
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - Qiyong Gong
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China
| | - John A Sweeney
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, USA
| | - Su Lui
- Department of Radiology, the Center for Medical Imaging, West China Hospital of Sichuan University, China.
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29
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Lei D, Pinaya WHL, Young J, van Amelsvoort T, Marcelis M, Donohoe G, Mothersill DO, Corvin A, Vieira S, Huang X, Lui S, Scarpazza C, Arango C, Bullmore E, Gong Q, McGuire P, Mechelli A. Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp 2019; 41:1119-1135. [PMID: 31737978 PMCID: PMC7268084 DOI: 10.1002/hbm.24863] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 10/23/2019] [Accepted: 10/31/2019] [Indexed: 02/05/2023] Open
Abstract
Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting‐state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low‐frequency fluctuation, regional homogeneity and two connectome‐wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10‐fold cross‐validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.
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Affiliation(s)
- Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Jonathan Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands.,Mental Health Care Institute Eindhoven (GGzE), Eindhoven, The Netherlands
| | - Gary Donohoe
- School of Psychology & Center for neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - David O Mothersill
- School of Psychology & Center for neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Aiden Corvin
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK.,Department of General Psychology, University of Padua, Padua, Italy
| | - Celso Arango
- Child and Adolescent Department of Psychiatry, Hospital General Universitario Gregorio Marañon, School of Medicine, Universidad Complutense Madrid, IiSGM, CIBERSAM, Madrid, Spain
| | - Ed Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
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Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI. J Digit Imaging 2019; 31:252-261. [PMID: 28924878 DOI: 10.1007/s10278-017-0020-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI.
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Kotłowska A, Szefer P. Recent Advances and Challenges in Steroid Metabolomics for Biomarker Discovery. Curr Med Chem 2019; 26:29-45. [PMID: 29141530 DOI: 10.2174/0929867324666171113120810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Revised: 03/01/2017] [Accepted: 03/20/2017] [Indexed: 12/21/2022]
Abstract
BACKGROUND Steroid hormones belong to a group of low-molecular weight compounds which are responsible for maintenance of various body functions, thus, their accurate assessment is crucial for evaluation of biosynthetic defects. The development of reliable methods allowing disease diagnosis is essential to improve early detection of various disorders connected with altered steroidogenesis. Currently, the field of metabolomics offers several improvements in terms of sensitivity and specificity of the diagnostic methods when opposed to classical diagnostic approaches. The combination of hyphenated techniques and pattern recognition methods allows to carry out a comprehensive assessment of the slightest alterations in steroid metabolic pathways and can be applied as a tool for biomarker discovery. METHODS We have performed an extensive literature search applying various bibliographic databases for peer-reviewed articles concentrating on the applications of hyphenated techniques and pattern recognition methods incorporated into the steroid metabolomic approach for biomarker discovery. RESULTS The review discusses strengths, challenges and recent developments in steroidbased metabolomics. We present methods of sample collection and preparation, methods of separation and detection of steroid hormones in biological material, data analysis, and interpretation as well as examples of applications of steroid metabolomics for biomarker discovery (cancer, mental and central nervous system disorders, endocrine diseases, monitoring of drug therapy and doping control). CONCLUSION Information presented in this review will be valuable to anyone interested in the application of metabolomics for biomarker discovery with a special emphasis on disorders of steroid hormone synthesis and metabolism.
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Affiliation(s)
- Alicja Kotłowska
- Department of Food Sciences, Faculty of Pharmacy, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland
| | - Piotr Szefer
- Department of Food Sciences, Faculty of Pharmacy, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland
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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: 32] [Impact Index Per Article: 6.4] [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.
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Affiliation(s)
| | | | - Nevzat Tarhan
- 1 Uskudar University, Istanbul, Turkey.,2 NPIstanbul Hospital, Istanbul, Turkey
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Moghimi P, Lim KO, Netoff TI. Data Driven Classification Using fMRI Network Measures: Application to Schizophrenia. Front Neuroinform 2018; 12:71. [PMID: 30425631 PMCID: PMC6218612 DOI: 10.3389/fninf.2018.00071] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 09/24/2018] [Indexed: 11/13/2022] Open
Abstract
Using classification to identify biomarkers for various brain disorders has become a common practice among the functional MR imaging community. Typical classification pipeline includes taking the time series, extracting features from them, and using them to classify a set of patients and healthy controls. The most informative features are then presented as novel biomarkers. In this paper, we compared the results of single and double cross validation schemes on a cohort of 170 subjects with schizophrenia and healthy control subjects. We used graph theoretic measures as our features, comparing the use of functional and anatomical atlases to define nodes and the effect of prewhitening to remove autocorrelation trends. We found that double cross validation resulted in a 20% decrease in classification performance compared to single cross validation. The anatomical atlas resulted in higher classification results. Prewhitening resulted in a 10% boost in classification performance. Overall, a classification performance of 80% was obtained with a double-cross validation scheme using prewhitened time series and an anatomical brain atlas. However, reproducibility of classification within subjects across scans was surprisingly low and comparable to across subject classification rates, indicating that subject state during the short scan significantly influences the estimated features and classification performance.
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Affiliation(s)
- Pantea Moghimi
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
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35
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Chin R, You AX, Meng F, Zhou J, Sim K. Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging. Sci Rep 2018; 8:13858. [PMID: 30218016 PMCID: PMC6138658 DOI: 10.1038/s41598-018-32290-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 09/05/2018] [Indexed: 12/17/2022] Open
Abstract
Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging (MRI) data. However, these traditional techniques lack sensitivity and predictive value at the individual level. Machine-learning approaches have emerged as potential diagnostic and prognostic tools. We used an anatomically and spatially regularized support vector machine (SVM) framework to categorize schizophrenia and healthy individuals based on whole-brain gray matter densities estimated using voxel-based morphometry from structural MRI scans. The regularized SVM model yielded recognition accuracy of 86.6% in the training set of 127 individuals and validation accuracy of 83.5% in an independent set of 85 individuals. A sequential region-of-interest (ROI) selection step was adopted for feature selection, improving recognition accuracy to 92.0% in the training set and 89.4% in the validation set. The combined model achieved 96.6% sensitivity and 74.1% specificity. Seven ROIs were identified as the optimal discriminatory subset: the occipital fusiform gyrus, middle frontal gyrus, pars opercularis of the inferior frontal gyrus, anterior superior temporal gyrus, superior frontal gyrus, left thalamus and left lateral ventricle. These findings demonstrate the utility of spatial and anatomical priors in SVM for neuroimaging analyses in conjunction with sequential ROI selection in the recognition of schizophrenia.
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Affiliation(s)
- Rowena Chin
- Research Division, Institute of Mental Health, Singapore, 10 Buangkok View, Singapore, 539747, Singapore
| | - Alex Xiaobin You
- Health Services & Outcomes Research, National Healthcare Group, 3 Fusionopolis Link, Singapore, 138543, Singapore
| | - Fanwen Meng
- Health Services & Outcomes Research, National Healthcare Group, 3 Fusionopolis Link, Singapore, 138543, Singapore
| | - Juan Zhou
- Neuroscience & Behavioral Disorders Program, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Kang Sim
- Research Division, Institute of Mental Health, Singapore, 10 Buangkok View, Singapore, 539747, Singapore.
- West Region, Institute of Mental Health/Woodbridge Hospital, Singapore, 10 Buangkok View, Singapore, 539747, Singapore.
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36
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Mateos-Pérez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC. Structural neuroimaging as clinical predictor: A review of machine learning applications. NEUROIMAGE-CLINICAL 2018; 20:506-522. [PMID: 30167371 PMCID: PMC6108077 DOI: 10.1016/j.nicl.2018.08.019] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 01/22/2018] [Accepted: 08/09/2018] [Indexed: 11/26/2022]
Abstract
In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.
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Affiliation(s)
| | - Mahsa Dadar
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | | | - Yashar Zeighami
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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de Moura AM, Pinaya WHL, Gadelha A, Zugman A, Noto C, Cordeiro Q, Belangero SI, Jackowski AP, Bressan RA, Sato JR. Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach. Psychiatry Res Neuroimaging 2018; 275:14-20. [PMID: 29548527 DOI: 10.1016/j.pscychresns.2018.03.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 02/27/2018] [Accepted: 03/02/2018] [Indexed: 01/16/2023]
Abstract
In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) or healthy controls (HC), from a schizophrenia model perspective. Brain regions volumetric data were estimated by using MRI images of SCZ and FEP patients and HC. First, we evaluated the MLDA performance in discriminating SCZ from controls, which provided a score based on a model for changes in brain structure in SCZ. In the following, we compared the volumetric patterns of FEP patients with patterns of SCZ and healthy controls using these scores. The FEP group had a score distribution more similar to patients with schizophrenia (p-value = .461; Cohen's d=-.15) in comparison with healthy subjects (p-value=.003; Cohen's d = .62). Structures related to the limbic system and the circuitry involved in goal-directed behaviours were the most discriminant regions. There is a distinct pattern of volumetric changes in patients with schizophrenia in contrast to healthy controls, and this pattern seem to be detectable already in FEP.
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Affiliation(s)
- Adriana Miyazaki de Moura
- Center of Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Walter Hugo Lopez Pinaya
- Center of Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Ary Gadelha
- Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil
| | - André Zugman
- Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil
| | - Cristiano Noto
- Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil
| | - Quirino Cordeiro
- Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil; Department of Psychiatry, Medical School of Santa Casa de São Paulo, São Paulo, Brazil
| | - Sintia Iole Belangero
- Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil; Department of Morphology and Genetics. Federal University of São Paulo, São Paulo, Brazil
| | - Andrea P Jackowski
- Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil
| | - Rodrigo A Bressan
- Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil
| | - João Ricardo Sato
- Center of Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil; Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil.
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Polimeni JR, Renvall V, Zaretskaya N, Fischl B. Analysis strategies for high-resolution UHF-fMRI data. Neuroimage 2018; 168:296-320. [PMID: 28461062 PMCID: PMC5664177 DOI: 10.1016/j.neuroimage.2017.04.053] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Revised: 04/21/2017] [Accepted: 04/22/2017] [Indexed: 12/22/2022] Open
Abstract
Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.
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Affiliation(s)
- Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States.
| | - Ville Renvall
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Natalia Zaretskaya
- Centre for Integrative Neuroscience, Department of Psychology, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
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Latha M, Kavitha G. Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2018; 31:483-499. [PMID: 29397450 DOI: 10.1007/s10334-018-0674-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Revised: 01/05/2018] [Accepted: 01/09/2018] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. MATERIALS AND METHODS T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. RESULTS The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). CONCLUSION A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.
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Affiliation(s)
- Manohar Latha
- Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India.
| | - Ganesan Kavitha
- Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India
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40
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He L, Li H, Holland SK, Yuan W, Altaye M, Parikh NA. Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework. NEUROIMAGE-CLINICAL 2018; 18:290-297. [PMID: 29876249 PMCID: PMC5987842 DOI: 10.1016/j.nicl.2018.01.032] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 01/22/2018] [Accepted: 01/24/2018] [Indexed: 12/15/2022]
Abstract
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in adults and older children have shown that abnormal network properties may be useful as discriminative features and have exploited machine learning models for early diagnosis in a variety of neurological conditions. However, analogous studies in neonates are rare and with limited significant findings. In this paper, we propose an artificial neural network (ANN) framework for early prediction of cognitive deficits in very preterm infants based on functional connectome data from resting state fMRI. Specifically, we conducted feature selection via stacked sparse autoencoder and outcome prediction via support vector machine (SVM). The proposed ANN model was unsupervised learned using brain connectome data from 884 subjects in autism brain imaging data exchange database and SVM was cross-validated on 28 very preterm infants (born at 23-31 weeks of gestation and without brain injury; scanned at term-equivalent postmenstrual age). Using 90 regions of interests, we found that the ANN model applied to functional connectome data from very premature infants can predict cognitive outcome at 2 years of corrected age with an accuracy of 70.6% and area under receiver operating characteristic curve of 0.76. We also noted that several frontal lobe and somatosensory regions, significantly contributed to prediction of cognitive deficits 2 years later. Our work can be considered as a proof of concept for utilizing ANN models on functional connectome data to capture the individual variability inherent in the developing brains of preterm infants. The full potential of ANN will be realized and more robust conclusions drawn when applied to much larger neuroimaging datasets, as we plan to do.
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Affiliation(s)
- Lili He
- Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
| | - Hailong Li
- Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Scott K Holland
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Weihong Yuan
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Mekibib Altaye
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- Perinatal Institute, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
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41
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Topcu-Yilmaz P, Aydin M, Cetin Ilhan B. Evaluation of retinal nerve fiber layer, macular, and choroidal thickness in schizophrenia: spectral optic coherence tomography findings. PSYCHIAT CLIN PSYCH 2018. [DOI: 10.1080/24750573.2018.1426693] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Pinar Topcu-Yilmaz
- Department of Ophthalmology, Ankara Numune Research and Training Hospital, Ankara, Turkey
| | - Memduha Aydin
- Department of Psychiatry, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Bilge Cetin Ilhan
- Department of Psychiatry, Konya Research and Training Hospital, Konya, Turkey
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42
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Abstract
Imaging genetics is a research methodology studying the effect of genetic variation on brain structure, function, behavior, and risk for psychopathology. Since the early 2000s, imaging genetics has been increasingly used in the research of schizophrenia (SZ). SZ is a severe mental disorder with no precise knowledge of its underlying neurobiology, however, new genetic and neurobiological data generate a climate for new avenues. The accumulating data of genome wide association studies (GWAS) continuously decode SZ risk genes. Global neuroimaging consortia produce collections of brain phenotypes from tens of thousands of people. In this context, imaging genetics will be strategically important both for the validation and discovery of SZ related findings. Thus, the study of GWAS supported risk variants as candidate genes to validate by neuroimaging is one trend. The study of epigenetic differences in relation to variations of brain phenotypes and the study of large scale multivariate analysis of genome wide and brain wide associations are other trends. While these studies hold a big potential for understanding the neurobiology of SZ, the problem of reproducibility appears as a major challenge, which requires standardizations in study designs and compensations of methodological limitations such as sensitivity and specificity. On the other hand, advancements of neuroimaging, optical and electron microscopy along with the use of genetically encoded fluorescent probes and robust statistical approaches will not only catalyze integrative methodologies but also will help better design the imaging genetics studies. In this invited paper, I will discuss the current perspective of imaging genetics and emerging opportunities of SZ research.
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Affiliation(s)
- Ayla Arslan
- Faculty of Engineering and Natural Sciences, Department of Genetics and Bioengineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina; Faculty of Engineering and Natural Sciences, Department of Molecular Biology and Genetics, Uskudar University, Istanbul, Turkey.
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43
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Hunter SA, Lawrie SM. Imaging and Genetic Biomarkers Predicting Transition to Psychosis. Curr Top Behav Neurosci 2018; 40:353-388. [PMID: 29626338 DOI: 10.1007/7854_2018_46] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The search for diagnostic and prognostic biomarkers in schizophrenia care and treatment is the focus of many within the research community. Longitudinal cohorts of patients presenting at elevated genetic and clinical risk have provided a wealth of data that has informed our understanding of the development of schizophrenia and related psychotic disorders.Imaging follow-up of high-risk cohorts has demonstrated changes in cerebral grey matter of those that eventually transition to schizophrenia that predate the onset of symptoms and evolve over the course of illness. Longitudinal follow-up studies demonstrate that observed grey matter changes can be employed to differentiate those who will transition to schizophrenia from those who will not prior to the onset of the disorder.In recent years our understanding of the genetic makeup of schizophrenia has advanced significantly. The development of modern analysis techniques offers researchers the ability to objectively quantify genetic risk; these have been successfully applied within a high-risk paradigm to assist in differentiating between high-risk individuals who will subsequently become unwell and those who will not.This chapter will discuss the application of imaging and genetic biomarkers within high-risk groups to predict future transition to schizophrenia and related psychotic disorders. We aim to provide an overview of current approaches focussing on grey matter changes that are predictive of future transition to illness, the developing field of genetic risk scores and other methods being developed to aid clinicians in diagnosis and prognosis.
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Affiliation(s)
- Stuart A Hunter
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK.
| | - Stephen M Lawrie
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
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44
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Chen YJ, Liu CM, Hsu YC, Lo YC, Hwang TJ, Hwu HG, Lin YT, Tseng WYI. Individualized prediction of schizophrenia based on the whole-brain pattern of altered white matter tract integrity. Hum Brain Mapp 2017; 39:575-587. [PMID: 29080229 DOI: 10.1002/hbm.23867] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 10/08/2017] [Accepted: 10/17/2017] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND A schizophrenia diagnosis relies on characteristic symptoms identified by trained physicians, and is thus prone to subjectivity. This study developed a procedure for the individualized prediction of schizophrenia based on whole-brain patterns of altered white matter tract integrity. METHODS The study comprised training (108 patients and 144 controls) and testing (60 patients and 60 controls) groups. Male and female participants were comparable in each group and were analyzed separately. All participants underwent diffusion spectrum imaging of the head, and the data were analyzed using the tract-based automatic analysis method to generate a standardized two-dimensional array of white matter tract integrity, called the connectogram. Unique patterns in the connectogram that most accurately identified schizophrenia were systematically reviewed in the training group. Then, the diagnostic performance of the patterns was individually verified in the testing group by using receiver-operating characteristic curve analysis. RESULTS The performance was high in men (accuracy = 0.85) and satisfactory in women (accuracy = 0.75). In men, the pattern was located in discrete fiber tracts, as has been consistently reported in the literature; by contrast, the pattern was widespread over all tracts in women. These distinct patterns suggest that there is a higher variability in the microstructural alterations in female patients than in male patients. CONCLUSIONS The individualized prediction of schizophrenia is feasible based on the different whole-brain patterns of tract integrity. The optimal masks and their corresponding regions in the fiber tracts could serve as potential imaging biomarkers for schizophrenia. Hum Brain Mapp 39:575-587, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Yu-Jen Chen
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chih-Min Liu
- Department of Psychiatry, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.,Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yung-Chin Hsu
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yu-Chun Lo
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.,Institute for Neural Regenerative Medicine, Taipei Medical University, Taipei, Taiwan
| | - Tzung-Jeng Hwang
- Department of Psychiatry, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.,Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hai-Gwo Hwu
- Department of Psychiatry, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.,Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yi-Tin Lin
- Department of Psychiatry, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Wen-Yih Isaac Tseng
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.,Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan.,Molecular Imaging Center, National Taiwan University, Taipei, Taiwan.,Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
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45
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Muzyk AJ, Gagliardi JP, Rakesh G, Jiroutek MR, Radhakrishnan R, Pae CU, Masand PS, Szabo ST. Development of a Diverse Learning Experience for Diverse Psychiatry Resident Needs: A Four-Year Biological Psychiatry Curriculum Incorporating Principles of Neurobiology, Psychopharmacology, and Evidence-Based Practice. Psychiatry Investig 2017; 14:289-297. [PMID: 28539947 PMCID: PMC5440431 DOI: 10.4306/pi.2017.14.3.289] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 03/15/2016] [Accepted: 06/03/2016] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE A clinically relevant approach to patient care grounded in neurobiological constructs and evidence based practice which emphasizes a relevant psychopharmacology is needed to optimally train psychiatry residents. METHODS We implemented a biological psychiatry course that now incorporates neurobiology, psychopharmacology, and evidence-based practice in conjunction with a Research Domain Criteria (RDoC) perspective. A survey launched prior to course implementation and following each class session, served as the outcome metric of residents' attitudes toward the new curriculum and followed a baseline attitudinal survey designed to evaluate the program. RESULTS Greater than 90% of the psychiatry residents at Duke University who took the attitudinal survey agreed or strongly agreed with needing a course that helped them develop an understanding of neurobiology, psychopharmacology, and evidence-based practice concepts. Most residents also indicated a less than adequate understanding of the neurobiology and psychopharmacology of psychiatric disorders prior to sessions. CONCLUSION Our biological psychiatry curriculum was associated with enthusiasm among residents regarding the incorporation of neurobiology, psychopharmacology, and evidence-based practice into course topics and discussions. A biological psychiatry curriculum with integrated neurobiology and psychopharmacology built on an evidence base approach is possible, well-received, and needed in training of future psychiatrists.
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Affiliation(s)
- Andrew J Muzyk
- Department of Pharmacy Practice, Campbell University School of Pharmacy and Health Sciences, Buies Creek, NC, USA
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, USA
| | - Jane P Gagliardi
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Gopalkumar Rakesh
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, USA
| | - Michael R Jiroutek
- Department of Clinical Research, Campbell University College of Pharmacy & Health Sciences, Buies Creek, NC, USA
| | | | - Chi-Un Pae
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, USA
- Department of Psychiatry, The Catholic University of Korea College of Medicine, Seoul, Korea
| | - Prakash S Masand
- Academic Medicine Education Institute, Duke-NUS Medical School, Singapore
- Global Medical Education, New York, NY, USA
| | - Steven T Szabo
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, USA
- Mental Health Service Line, Veterans Affairs Medical Center, Durham, NC, USA
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46
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Dluhoš P, Schwarz D, Cahn W, van Haren N, Kahn R, Španiel F, Horáček J, Kašpárek T, Schnack H. Multi-center machine learning in imaging psychiatry: A meta-model approach. Neuroimage 2017; 155:10-24. [PMID: 28428048 DOI: 10.1016/j.neuroimage.2017.03.027] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 03/06/2017] [Accepted: 03/14/2017] [Indexed: 01/17/2023] Open
Abstract
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues. Moreover, in the case of very large samples, the computational complexity might become too large. The solution to this problem could be distributed learning. In this paper we investigated the possibility to create a meta-model by combining support vector machines (SVM) classifiers trained on the local datasets, without the need for sharing medical images or any other personal data. Validation was done in a 4-center setup comprising of 480 first-episode schizophrenia patients and healthy controls in total. We built SVM models to separate patients from controls based on three different kinds of imaging features derived from structural MRI scans, and compared models built on the joint multicenter data to the meta-models. The results showed that the combined meta-model had high similarity to the model built on all data pooled together and comparable classification performance on all three imaging features. Both similarity and performance was superior to that of the local models. We conclude that combining models is thus a viable alternative that facilitates data sharing and creating bigger and more informative models.
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Affiliation(s)
- Petr Dluhoš
- Behavioural and Social Neuroscience Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Department of Psychiatry, University Hospital Brno and Masaryk University, Brno, Czech Republic.
| | - Daniel Schwarz
- Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic
| | - Wiepke Cahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - René Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Filip Španiel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Jiří Horáček
- National Institute of Mental Health, Klecany, Czech Republic
| | - Tomáš Kašpárek
- Behavioural and Social Neuroscience Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Department of Psychiatry, University Hospital Brno and Masaryk University, Brno, Czech Republic
| | - Hugo Schnack
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
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47
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Zarogianni E, Storkey AJ, Johnstone EC, Owens DGC, Lawrie SM. Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features. Schizophr Res 2017; 181:6-12. [PMID: 27613509 DOI: 10.1016/j.schres.2016.08.027] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 08/29/2016] [Accepted: 08/29/2016] [Indexed: 01/11/2023]
Abstract
To date, there are no reliable markers for predicting onset of schizophrenia in individuals at high risk (HR). Substantial promise is, however, shown by a variety of pattern classification approaches to neuroimaging data. Here, we examined the predictive accuracy of support vector machine (SVM) in later diagnosing schizophrenia, at a single-subject level, using a cohort of HR individuals drawn from multiply affected families and a combination of neuroanatomical, schizotypal and neurocognitive variables. Baseline structural magnetic resonance imaging (MRI), schizotypal and neurocognitive data from 17 HR subjects, who subsequently developed schizophrenia and a matched group of 17 HR subjects who did not make the transition, yet had psychotic symptoms, were included in the analysis. We employed recursive feature elimination (RFE), in a nested cross-validation scheme to identify the most significant predictors of disease transition and enhance diagnostic performance. Classification accuracy was 94% when a self-completed measure of schizotypy, a declarative memory test and structural MRI data were combined into a single learning algorithm; higher than when either quantitative measure was used alone. The discriminative neuroanatomical pattern involved gray matter volume differences in frontal, orbito-frontal and occipital lobe regions bilaterally as well as parts of the superior, medial temporal lobe and cerebellar regions. Our findings suggest that an early SVM-based prediction of schizophrenia is possible and can be improved by combining schizotypal and neurocognitive features with neuroanatomical variables. However, our predictive model needs to be tested by classifying a new, independent HR cohort in order to estimate its validity.
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Affiliation(s)
- Eleni Zarogianni
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK.
| | - Amos J Storkey
- Institute for Adaptive and Neural Computation, University of Edinburgh, UK
| | - Eve C Johnstone
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK
| | - David G C Owens
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK
| | - Stephen M Lawrie
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK
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48
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Pergola G, Trizio S, Di Carlo P, Taurisano P, Mancini M, Amoroso N, Nettis MA, Andriola I, Caforio G, Popolizio T, Rampino A, Di Giorgio A, Bertolino A, Blasi G. Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia. Schizophr Res 2017; 180:13-20. [PMID: 27449252 DOI: 10.1016/j.schres.2016.07.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Revised: 07/05/2016] [Accepted: 07/07/2016] [Indexed: 12/19/2022]
Abstract
Previous evidence suggests reduced thalamic grey matter volume (GMV) in patients with schizophrenia (SCZ). However, it is not considered an intermediate phenotype for schizophrenia, possibly because previous studies did not assess the contribution of individual thalamic nuclei and employed univariate statistics. Here, we hypothesized that multivariate statistics would reveal an association of GMV in different thalamic nuclei with familial risk for schizophrenia. We also hypothesized that accounting for the heterogeneity of thalamic GMV in healthy controls would improve the detection of subjects at familial risk for the disorder. We acquired MRI scans for 96 clinically stable SCZ, 55 non-affected siblings of patients with schizophrenia (SIB), and 249 HC. The thalamus was parceled into seven regions of interest (ROIs). After a canonical univariate analysis, we used GMV estimates of thalamic ROIs, together with total thalamic GMV and premorbid intelligence, as features in Random Forests to classify HC, SIB, and SCZ. Then, we computed a Misclassification Index for each individual and tested the improvement in SIB detection after excluding a subsample of HC misclassified as patients. Random Forests discriminated SCZ from HC (accuracy=81%) and SIB from HC (accuracy=75%). Left anteromedial thalamic volumes were significantly associated with both multivariate classifications (p<0.05). Excluding HC misclassified as SCZ improved greatly HC vs. SIB classification (Cohen's d=1.39). These findings suggest that multivariate statistics identify a familial background associated with thalamic GMV reduction in SCZ. They also suggest the relevance of inter-individual variability of GMV patterns for the discrimination of individuals at familial risk for the disorder.
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Affiliation(s)
- Giulio Pergola
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Silvestro Trizio
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Pasquale Di Carlo
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Paolo Taurisano
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Marina Mancini
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Nicola Amoroso
- National Institute of Nuclear of Physics-Branch of Bari, Via E. Orabona 4, 70125 Bari, Italy; Interuniversity Department of Physics 'M. Merlin', University of Bari 'Aldo Moro', Via E. Orabona 4, 70125 Bari, Italy
| | - Maria Antonietta Nettis
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Ileana Andriola
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Grazia Caforio
- Psychiatry Unit, Bari University Hospital, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Teresa Popolizio
- IRCCS "Casa Sollievo della Sofferenza", Viale Cappuccini, 1, I-71013 San Giovanni Rotondo, Italy
| | - Antonio Rampino
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy; Psychiatry Unit, Bari University Hospital, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Annabella Di Giorgio
- IRCCS "Casa Sollievo della Sofferenza", Viale Cappuccini, 1, I-71013 San Giovanni Rotondo, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy; Psychiatry Unit, Bari University Hospital, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Giuseppe Blasi
- Psychiatry Unit, Bari University Hospital, Piazza Giulio Cesare 11, 70124, Bari, Italy; IRCCS "Casa Sollievo della Sofferenza", Viale Cappuccini, 1, I-71013 San Giovanni Rotondo, Italy.
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 513] [Impact Index Per Article: 73.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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50
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Vieira S, Pinaya WHL, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci Biobehav Rev 2017; 74:58-75. [PMID: 28087243 DOI: 10.1016/j.neubiorev.2017.01.002] [Citation(s) in RCA: 259] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Revised: 12/22/2016] [Accepted: 01/04/2017] [Indexed: 12/29/2022]
Abstract
Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition. DL differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecutive nonlinear transformations, achieving increasingly higher levels of abstraction and complexity. Given its ability to detect abstract and complex patterns, DL has been applied in neuroimaging studies of psychiatric and neurological disorders, which are characterised by subtle and diffuse alterations. Here we introduce the underlying concepts of DL and review studies that have used this approach to classify brain-based disorders. The results of these studies indicate that DL could be a powerful tool in the current search for biomarkers of psychiatric and neurologic disease. We conclude our review by discussing the main promises and challenges of using DL to elucidate brain-based disorders, as well as possible directions for future research.
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Affiliation(s)
- Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom.
| | - Walter H L Pinaya
- Centre of Mathematics, Computation, and Cognition, Universidade Federal do ABC, Rua Arcturus, Jardim Antares, São Bernardo do Campo, SP CEP 09.606-070, Brazil
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
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