Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition (original) (raw)
Related papers
2011
Background: Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivariate machine learning methods have demonstrated the feasibility to predict illness onset in clinically defined at-risk individuals using structural magnetic resonance imaging (MRI) data. However, it remains unclear whether these findings could be replicated in independent populations. Methods: We evaluated the performance of an MRI-based classification system in predicting disease conversion in atrisk individuals recruited within the prospective FePsy (Fru¨herkennung von Psychosen) study at the University of Basel, Switzerland. Pairwise and multigroup biomarkers were constructed using the MRI data of 22 healthy volunteers, 16/21 at-risk subjects with/without a subsequent disease conversion. Diagnostic performance was measured in unseen test cases using repeated nested cross-validation. Results: The classification accuracies in the ''healthy controls (HCs) vs converters,'' ''HCs vs nonconverters,'' and ''converters vs nonconverters'' analyses were 92.3%, 66.9%, and 84.2%, respectively. A positive likelihood ratio of 6.5 in the converters vs nonconverters analysis indicated a 40% increase in diagnostic certainty by applying the biomarker to an at-risk population with a transition rate of 43%. The neuroanatomical decision functions underlying these results particularly involved the prefrontal perisylvian and subcortical brain structures. Conclusions: Our findings suggest that the early prediction of psychosis may be reliably enhanced using neuroanatomical pattern recognition operating at the single-subject level. These MRI-based biomarkers may have the potential to identify individuals at the highest risk of developing psychosis, and thus may promote informed clinical strategies aiming at preventing the full manifestation of the disease.
PloS one, 2017
A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps. All possible combinations of algorithms and data features were consi...
Currently, most of the classification studies of psychosis focused on chronic patients and employed single machine learning approaches. To overcome these limitations , we here compare, to our best knowledge for the first time, different classification methods of first-episode psy-chosis (FEP) using multi-modal imaging data exploited on several cortical and subcortical structures and white matter fiber bundles. 23 FEP patients and 23 age-, gender-, and race-matched healthy participants were included in the study. An innovative multivariate approach based on multiple kernel learning (MKL) methods was implemented on structural MRI and diffusion tensor imaging. MKL provides the best classification performances in comparison with the more widely used support vector machine, enabling the definition of a reliable automatic decisional system based on the integration of multi-modal imaging information. Our results show a discrimination accuracy greater than 90 % between healthy subjects and patients with FEP. Regions with an accuracy greater than 70 % on different imaging sources and measures were middle and superior frontal gyrus, parahippocampal gyrus, uncinate fascicles, and cingulum. This study shows that multivariate machine learning approaches integrating multi-modal and multisource imaging data can classify FEP patients with high accuracy. Interestingly, specific grey matter structures and white matter bundles reach high classification reliability when using different imaging modalities and indices , potentially outlining a prefronto-limbic network impaired in FEP with particular regard to the right hemisphere.
Psychiatry research. Neuroimaging, 2018
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...
2014
Although structural magnetic resonance imaging (MRI) has revealed partly non-overlapping brain abnormalities in schizophrenia and bipolar disorder, it is unknown whether structural MRI scans can be used to separate individuals with schizophrenia from those with bipolar disorder. An algorithm capable of discriminating between these two disorders could become a diagnostic aid for psychiatrists. Here, we scanned 66 schizophrenia patients, 66 patients with bipolar disorder and 66 healthy subjects on a 1.5 T MRI scanner. Three support vector machines were trained to separate patients with schizophrenia from healthy subjects, patients with schizophrenia from those with bipolar disorder, and patients with bipolar disorder from healthy subjects, respectively, based on their gray matter density images. The predictive power of the models was tested using cross-validation and in an independent validation set of 46 schizophrenia patients, 47 patients with bipolar disorder and 43 healthy subjects scanned on a 3 T MRI scanner. Schizophrenia patients could be separated from healthy subjects with an average accuracy of 90%. Additionally, schizophrenia patients and patients with bipolar disorder could be distinguished with an average accuracy of 88%.The model delineating bipolar patients from healthy subjects was less accurate, correctly classifying 67% of the healthy subjects and only 53% of the patients with bipolar disorder. In the latter group, lithium and antipsychotics use had no influence on the classification results. Application of the 1.5 T models on the 3 T validation set yielded average classification accuracies of 76% (healthy vs schizophrenia), 66% (bipolar vs schizophrenia) and 61% (healthy vs bipolar). In conclusion, the accurate separation of schizophrenia from bipolar patients on the basis of structural MRI scans, as demonstrated here, could be of added value in the differential diagnosis of these two disorders. The results also suggest that gray matter pathology in schizophrenia and bipolar disorder differs to such an extent that they can be reliably differentiated using machine learning paradigms.
Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia
Recent neuroanatomical pattern classification studies have attempted to individually classify cases with psychotic disorders using morphometric MRI data in an automated fashion. However, this approach has not been tested in population-based samples, in which variable patterns of comorbidity and disease course are typically found. We aimed to evaluate the diagnostic accuracy (DA) of the above technique to discriminate between incident cases of first-episode schizophrenia identified in a circumscribed geographical region over a limited period of time, in comparison with next-door healthy controls. Sixty-two cases of first-episode schizophrenia or schizophreniform disorder and 62 age, gender and educationally-matched controls underwent 1.5 T MRI scanning at baseline, and were naturalistically followed-up over 1 year. T1-weighted images were used to train a high-dimensional multivariate classifier, and to generate both spatial maps of the discriminative morphological patterns between groups and ROC curves. The spatial map discriminating first-episode schizo-phrenia patients from healthy controls revealed a complex pattern of regional volumetric abnormalities in the former group, affecting fronto-temporal-occipital gray and white matter regions bilaterally, including the inferior fronto-occipital fasciculus, as well as the third and lateral ventricles. However, an overall modest DA (73.4%) was observed for the individual discrimination between first-episode schizophrenia patients and controls, and the classifier failed to predict 1-year prognosis (remitting versus non-remitting course) of first-episode schizophrenia (DA = 58.3%). In conclusion, using a " real world " sample recruited with epidemiological methods, the application of a neuroanatomical pattern classifier afforded only modest DA to classify first-episode schizophrenia subjects and next-door healthy controls, and poor discriminative power to predict the 1-year prognosis of first-episode schizophrenia.
2012
The at-risk mental state for psychosis (ARMS) and the first episode of psychosis have been associated with structural brain abnormalities that could aid in the individualized early recognition of psychosis. However, it is unknown whether the development of these brain alterations predates the clinical deterioration of at-risk individuals, or alternatively, whether it parallels the transition to psychosis at the single-subject level. Methods: We evaluated the performance of an magnetic resonance imaging (MRI)-based classification system in classifying disease stages from at-risk individuals with subsequent transition to psychosis (ARMS-T) and patients with first-episode psychosis (FE). Pairwise and multigroup biomarkers were constructed using the structural MRI data of 22 healthy controls (HC), 16 ARMS-T and 23 FE subjects. The performance of these biomarkers was measured in unseen test cases using repeated nested cross-validation. Results: The classification accuracies in the HC vs FE, HC vs ARMS-T, and ARMS-T vs FE analyses were 86.7%, 80.7%, and 80.0%, respectively. The neuroanatomical decision functions underlying these discriminative results particularly involved the frontotemporal, cingulate, cerebellar, and subcortical brain structures. Conclusions: Our findings suggest that structural brain alterations accumulate at the onset of psychosis and occur even before transition to psychosis allowing for the single-subject differentiation of the prodromal and first-episode stages of the disease. Pattern regression techniques facilitate an accurate prediction of these structural brain dynamics at the early stage of psychosis, potentially allowing for the early recognition of individuals at risk of developing psychosis.
Journal of Neural Transmission, 2014
Currently, most of the classification studies of psychosis focused on chronic patients and employed single machine learning approaches. To overcome these limitations, we here compare, to our best knowledge for the first time, different classification methods of first-episode psychosis (FEP) using multi-modal imaging data exploited on several cortical and subcortical structures and white matter fiber bundles. 23 FEP patients and 23 age-, gender-, and race-matched healthy participants were included in the study. An innovative multivariate approach based on multiple kernel learning (MKL) methods was implemented on structural MRI and diffusion tensor imaging. MKL provides the best classification performances in comparison with the more widely used support vector machine, enabling the definition of a reliable automatic decisional system based on the integration of multi-modal imaging information. Our results show a discrimination accuracy greater than 90 % between healthy subjects and patients with FEP. Regions with an accuracy greater than 70 % on different imaging sources and measures were middle and superior frontal gyrus, parahippocampal gyrus, uncinate fascicles, and cingulum. This study shows that multivariate machine learning approaches integrating multi-modal and multisource imaging data can classify FEP patients with high accuracy. Interestingly, specific grey matter structures and white matter bundles reach high classification reliability when using different imaging modalities and indices, potentially outlining a prefronto-limbic network impaired in FEP with particular regard to the right hemisphere.
Frontiers in Psychiatry, 2012
Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI). However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays, and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest (RF), a machine learning method, we classified 98 childhood onset schizophrenia (COS) patients and 99 age, sex, and ethnicity-matched healthy controls. We also used RF to estimate the probability of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation (CNV) associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p = 0.0004) and fewer developmental delays (p = 0.02). Presence of CNV was associated with lower probability of being classified as schizophrenia (p = 0.001). The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusion: Schizophrenia and control groups can be well classified using RF and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk.