scholarly journals Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence

2019 ◽  
Vol 46 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Sandra Vieira ◽  
Qi-yong Gong ◽  
Walter H L Pinaya ◽  
Cristina Scarpazza ◽  
Stefania Tognin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cortical Thickness ◽  
Gray Matter Volume ◽  
Diagnostic Value ◽  
First Episode Psychosis ◽  
Current Evidence ◽  
First Episode ◽  
Structural Neuroimaging ◽  
Individual Level ◽  
Episode Psychosis

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.

scholarly journals M167. MACHINE LEARNING CLASSIFICATION OF FIRST-EPISODE PSYCHOSIS USING CORTICAL THICKNESS IN A LARGE MULTICENTER MRI STUDY

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S200-S200
Author(s):  
Alessandro Pigoni ◽  
Dominic Dwyer ◽  
Letizia Squarcina ◽  
Stefan Borgwardt ◽  
Benedicto Crespo-Facorro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cortical Thickness ◽  
Cross Validation ◽  
Area Under The Curve ◽  
First Episode Psychosis ◽  
First Episode ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Episode Psychosis ◽  
Dependent Variables

Abstract Background Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness data, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between the predictions and clinical variables. Methods 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI (sMRI) session and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20484 vertices) were extracted. Supervised (linear Support Vector Machine) classification was used to differentiate FEP from HC, within a repeated nested Cross-Validation (CV) framework through the NeuroMiner software. In both inner and outer CVs, a 10-fold CV cycle was employed. We performed repeated nested CV at the outer cross-validation cycle by randomly permuting the participants within their groups (10 permutations) and repeating the CV cycle for each of these permutations. As feature preprocessing, regression of covariates (age, sex, and site), Principal Component Analysis and Scaling were applied. All preprocessing steps were implemented within the CV. Further analyses were conducted by stratifying the sample for MRI scanner, sex and by performing random resampling with increasingly reduced sample sizes. Results Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy (BAC) of 66.2% and an Area Under the Curve of 72%, compared to a BAC of 59% and an Area Under the Curve of 61% obtained with the ROI-based approach. The two BACs were significantly different based on the McNemar’s Test. By stratifying our sample for MRI scanner, we increased the overall BAC to more than 70% and we also increased generalizability across sites. Temporal areas resulted the most influential regions in the classification. The predictive decision scores presented significant correlations with age at onset, duration of treatment and the presence of affective vs non-affective psychosis. Discussion Cortical thickness could represent a valid measure to classify FEP subjects, showing temporal areas as potential markers in the early stages of psychosis. The assessment of site-dependent variables allowed us to increase the across-site generalizability of the model, thus attempting to address an important machine learning limitation, especially in the framework of large multi-site cohort and big data analysis.

scholarly journals Cortical thickness and surface area correlates with cognitive dysfunction among first-episode psychosis patients

2016 ◽  
Vol 46 (10) ◽  
pp. 2145-2155 ◽  
Author(s):  
L. Haring ◽  
A. Müürsepp ◽  
R. Mõttus ◽  
P. Ilves ◽  
K. Koch ◽  
...  
Keyword(s):  
Structure Function ◽  
Surface Area ◽  
Cortical Thickness ◽  
Brain Structure ◽  
Brain Mri ◽  
Brain Regions ◽  
First Episode Psychosis ◽  
First Episode ◽  
Neurocognitive Performance ◽  
Episode Psychosis

BackgroundIn studies using magnetic resonance imaging (MRI), some have reported specific brain structure–function relationships among first-episode psychosis (FEP) patients, but findings are inconsistent. We aimed to localize the brain regions where cortical thickness (CTh) and surface area (cortical area; CA) relate to neurocognition, by performing an MRI on participants and measuring their neurocognitive performance using the Cambridge Neuropsychological Test Automated Battery (CANTAB), in order to investigate any significant differences between FEP patients and control subjects (CS).MethodExploration of potential correlations between specific cognitive functions and brain structure was performed using CANTAB computer-based neurocognitive testing and a vertex-by-vertex whole-brain MRI analysis of 63 FEP patients and 30 CS.ResultsSignificant correlations were found between cortical parameters in the frontal, temporal, cingular and occipital brain regions and performance in set-shifting, working memory manipulation, strategy usage and sustained attention tests. These correlations were significantly dissimilar between FEP patients and CS.ConclusionsSignificant correlations between CTh and CA with neurocognitive performance were localized in brain areas known to be involved in cognition. The results also suggested a disrupted structure–function relationship in FEP patients compared with CS.

scholarly journals Developing services for first-episode psychosis and the critical period

2005 ◽  
Vol 187 (S48) ◽  
pp. s91-s97 ◽  
Author(s):  
Jane Edwards ◽  
Meredith G. Harris ◽  
Swagata Bapat
Keyword(s):  
Evaluation Studies ◽  
Complex Interventions ◽  
Clinical Work ◽  
First Episode Psychosis ◽  
Early Psychosis ◽  
Current Evidence ◽  
First Episode ◽  
Episode Psychosis ◽  
Assessment And Treatment ◽  
Recent Evaluation

BackgroundProviding specialised services to individuals experiencing first-episode psychosis (FEP) is a relatively new endeavour.AimsTo overview developing services for newly diagnosed cases of FEP and the context in which they develop.MethodThis paper describes five model multi-element FEP programmes, outlines recent evaluation studies of FEP services, discusses current evidence gaps relating to the evaluation of complex interventions and specific interventions for FEP and illustrates attempts to examine aspects of clinical work practised at the Early Psychosis Prevention and Intervention Centre (EPPIC) in Melbourne, Australia.ResultsConsiderable progress has been made in terms of influencing practice in the assessment and treatment of early psychosis.ConclusionsThere is need for quality clinical and research efforts to inform and accelerate progress in this burgeoning field.

scholarly journals Brain structural correlates of functional capacity in first-episode psychosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Erkan Alkan ◽  
Geoff Davies ◽  
Kathy Greenwood ◽  
Simon L. Evans
Keyword(s):  
Functional Capacity ◽  
Gray Matter Volume ◽  
Brain Regions ◽  
First Episode Psychosis ◽  
First Episode ◽  
Control Group ◽  
Neural Basis ◽  
Antipsychotic Therapy ◽  
Brain Volumes ◽  
Episode Psychosis

Abstract Impaired functional capacity is a core feature of schizophrenia and presents even in first-episode psychosis (FEP) patients. Impairments in daily functioning tend to persist despite antipsychotic therapy but their neural basis is less clear. Previous studies suggest that volume loss in frontal cortex might be an important contributor, but findings are inconsistent. We aimed to comprehensively investigate the brain structural correlates of functional capacity in FEP using MRI and a reliable objective measure of functioning [University of California, San Diego Performance-Based Skills Assessment (UPSA)]. In a sample of FEP (n = 39) and a well-matched control group (n = 21), we measured cortical thickness, gray matter volume, and white matter tract integrity (fractional anisotropy, FA) within brain regions implicated by previous work. The FEP group had thinner cortex in various frontal regions and fusiform, and reduced FA in inferior longitudinal fasciculus (ILF). In FEP, poorer functional capacity correlated with reduced superior frontal volume and lower FA in left ILF. Importantly, frontal brain volumes and integrity of the ILF were identified as the structural correlates of functional capacity in FEP, controlling for other relevant factors. These findings enhance mechanistic understanding of functional capacity deficits in schizophrenia by specifying the underlying neural correlates. In future, this could help inform intervention strategies.

Structural Neuroimaging in First-Episode Psychosis: a review of neuroimaging practice in an early intervention service

2011 ◽  
Vol 26 (S2) ◽  
pp. 947-947
Author(s):  
S. Otero ◽  
R. Mehrotra
Keyword(s):  
Early Intervention ◽  
Statistical Significance ◽  
First Episode Psychosis ◽  
First Episode ◽  
Analysis Tool ◽  
Early Intervention Service ◽  
Structural Neuroimaging ◽  
Intervention Service ◽  
Episode Psychosis ◽  
Significant Difference

IntroductionThe UK NICE technology guidance “Structural Neuroimaging in First-Episode Psychosis” concludes that CT/MRI is not routinely recommended as an initial investigation for first-episode psychosis.ObjectivesTo evaluate the use of CT/MRI in a group of Early Intervention Service (EIS) patients with a first-episode psychosis aged 18–35 years at presentation.AimsTo develop practice guidelines for use of neuroimaging in first-episode psychosis.MethodsAll 107 patients registered with the EIS in Hounslow, London, UK, were eligible for inclusion in this review. Data was collected from the medical records and the Picture Archiving and Communications System. Data was analysed using a microsoft excel data analysis tool. Additionally, comparisons were made between the group of patients with normal scans and that with abnormal scans. Statistical significance was determined using the chi-squared method with a significance of P < 0.05.Results17 patients had documented neuroimaging results. 4 scans were abnormal. There was no significant difference between the group with normal and abnormal scans in terms of gender, abnormalities of physical/neurological health, blood tests and whether the patient had any additional medical conditions. Abnormal scan results did not influence treatment or outcome for any patient.ConclusionsThe abnormal scans were not correlated to clinical indices of history, examination and laboratory tests. Abnormal scans appear to have a low yield in terms of clinical effectiveness. The findings support selective use of neuroimaging in this cohort of patients. The indications for it usage would appear to rely on clinical judgement as well clinical findings.

Relationship between gray matter volume and clinical symtomatology in first-episode psychosis

NeuroImage ◽  
2001 ◽  
Vol 13 (6) ◽  
pp. 1084
Author(s):  
Katrine Pagsberg ◽  
William Baaré ◽  
Torben Mackeprang ◽  
Anne Marie Christensen ◽  
Tove Aarkrog ◽  
...  
Keyword(s):  
Gray Matter ◽  
Gray Matter Volume ◽  
First Episode Psychosis ◽  
First Episode ◽  
Matter Volume ◽  
Episode Psychosis

scholarly journals Predicting One-Year Outcome in First Episode Psychosis using Machine Learning

2018 ◽  
Author(s):  
Samuel Leighton ◽  
Rajeev Krishnadas ◽  
Kelly Chung ◽  
Alison Blair ◽  
Susie Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
First Episode Psychosis ◽  
Elastic Net ◽  
Training Dataset ◽  
Validation Dataset ◽  
First Episode ◽  
Episode Psychosis ◽  
Symptom Remission ◽  
One Year ◽  
Independent Cohort

BackgroundEarly illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes. Our study objective is to use routinely collected baseline demographic and clinical characteristics to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at one-year.Methods and findings83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009. Elastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at one-year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation. After excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with ROC area under curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS. A key limitation of this study is the small sample size (n) relative to the number of predictors (p), whereby p approaches n. The use of elastic net regularised regression rather than ordinary least squares regression helped circumvent this difficulty. Further, we did not have information for biological and additional social variables, such as nicotine dependence, which observational studies have linked to outcomes in psychosis. Conclusions and RelevanceUsing advanced statistical machine learning techniques we provide the first externally validated evidence, in a temporally and geographically independent cohort, for the ability to predict one-year EET status and symptom remission in individual FEP patients.

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