Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy

Abstract Background Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential of MRI in establishing a psychiatric diagnosis. Machine learning has previously been predominantly tested on gray-matter structural or functional MRI data. In this paper we used a machine learning classifier to differentiate patients with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls using diffusion tensor imaging. Methods We applied linear support-vector machine (SVM) and traditional tract based spatial statistics between group analyses to brain fractional anisotropy (FA) data from 77 FES and 77 age and sex matched healthy controls. We also evaluated the effects of medication and symptoms on the SVM classification. Results The SVM distinguished between patients and controls with significant accuracy of 62.34% (p = 0.005). Participants with FES showed widespread FA reductions relative to controls in a large cluster (N  = 56,647 voxels, corrected p = 0.002). The white matter regions, which contributed to the correct identification of participants with FES, overlapped with the regions, which showed lower FA in patients relative to controls. There was no association between the classification performance and medication or symptoms. Conclusions Our results provide a proof of concept that SVM might help differentiate FES patients early in the course of illness from healthy controls using white-matter fractional anisotropy. As there was no effect of medications or symptoms, the SVM classification seemed to be based on trait rather than state markers and appeared to capture the lower FA in FES participants relative to controls

Predicting bipolar risk scores using the volumes of hippocampal subfields and nuclei of the amygdala with a machine learning approach

Bipolar disorders (BD) are serious chronic mental disorders. 10-20% of patients suffering from BD commit suicide throughout their disease course (Müller-Oerlinghausen et al. 2002). A longer duration of untreated illness leads to more depressive and manic episodes and more suicidal behavior (Drancourt et al. 2013). Hence, early detection and treatment is crucial. Some attempts to detect BD ahead of diagnosis have already been made. For instance, the prognostic accuracy of two clinical interviews has been investigated: The Bipolar at Risk States Revised (BARS; Harrell's C = 0.777) and the Semistructured Interview of At Risk Bipolar States (SIBARS; Harrell's C = 0.742) (Fusar-Poli et al. 2018). Also, in a genetic study, polygenic risk scores differed significantly between control and at-risk groups, but not between at-risk and BD type-I groups (Smigielski et al. 2021). Adding additional data categories to the scales, in particular neuroimaging, could increase the prognostic accuracy and thus lead to an earlier illness detection and better clinical outcomes. With the aim of finding a valid biomarker for BD using neuroimaging, several structural abnormalities have already been identified (Arnone et al. 2009). A currently published mega-analysis with 4,698 participants comparing BD patients with healthy controls (HCs) found significantly smaller volumes of the hippocampus and its subfields (whole hippocampus, GC ML DG, CA4, CA3, CA1, subiculum, presubiculum, molecular layer HP, HATA and hippocampal tail) but not for other subfields (parasubiculum, fimbria and the hippocampal fissure) (Haukvik et al. 2022). Another study found some subfields to be smaller for both, BD and schizophrenia patients, compared to HCs (bilateral CA2/3, CA4/dentate gyrus, subiculum and right CA1) (Haukvik et al. 2015). Meanwhile, presubiculum volumes were smaller only in schizophrenia and, comparing schizophrenia with BD directly, the bilateral subiculum as well as the right presubiculum was found to be smaller for schizophrenia (Haukvik et al. 2015). In an investigation between different psychotic disorders, smaller subfield volumes could be found only in the bilateral CA2/3, the left presubiculum and the right CA4/DG, comparing psychotic bipolar disorder (Mathew et al. 2014). Now, we will investigate if there are observable differences already before any BD diagnosis. Another potentially interesting region for early detection of BD, with less literature to date, is the amygdala and its subnuclei. For instance, a decreased volume was found for the lateral and cortical nuclei, but not for the basal and accessory basal nuclei in BD patients compared to HC (Pantazopoulos et al. 2017). However, Bielau et al. (2005) found no significant differences for the whole amygdala volume in a post-mortem study between BD patients and HC. Thus, the volumes of segmented amygdala nuclei volumes should be considered as potential risk factors for BD. While traditional statistical group comparisons can show mean structural abnormalities, they lack practical clinical impact (Orru et al. 2012). A multivariable machine learning (ML) approach meanwhile allows to make individual inferences potentially useful in clinical prognostics. Thus, we will implement a support vector machine (SVM). This is a widely used algorithm, which classifies huge datasets into groups through weighting so-called features, i.e. structural volumes. These groups are here defined as risk states for BD through three independent assessment tools: EPIbipolar (Leopold et al. 2012), BPSS-P (Correll et al. 2014) and SIBARS (Fusar-Poli et al. 2018). In a currently submitted paper, we already investigated such a SVM classification. We used regional cortical thickness and surface area values as well as subcortical structural volumes (Mikolas et al. 2022), in submission, pre-registered at https://osf.io/c4hfn). Now, we will adapt this procedure to hippocampal subfields and nuclei of the amygdala

Young people at risk for developing bipolar disorder: Two-year findings from the multicenter prospective, naturalistic Early-BipoLife study

: Early identification and intervention of individuals with an increased risk for bipolar disorder (BD) may improve the course of illness and prevent long‑term consequences. Early-BipoLife, a multicenter, prospective, naturalistic study, examined risk factors of BD beyond family history in participants aged 15-35 years. At baseline, positively screened help-seeking participants (screenBD at-risk) were recruited at Early Detection Centers and in- and outpatient depression and attention-deficit/hyperactivity disorder (ADHD) settings, references (Ref) drawn from a representative cohort. Participants reported sociodemographics and medical history and were repeatedly examined regarding psychopathology and the course of risk factors. N = 1,083 screenBD at-risk and n = 172 Ref were eligible for baseline assessment. Within the first two years, n = 31 screenBD at-risk (2.9 %) and none of Ref developed a manifest BD. The cumulative transition risk was 0.0028 at the end of multistep assessment, 0.0169 at 12 and 0.0317 at 24 months (p = 0.021). The transition rate with a BD family history was 6.0 %, 4.7 % in the Early Phase Inventory for bipolar disorders (EPIbipolar), 6.6 % in the Bipolar Prodrome Interview and Symptom Scale-Prospective (BPSS-FP) and 3.2 % with extended Bipolar At-Risk - BARS criteria). In comparison to help-seeking young patients from psychosis detection services, transition rates in screenBD at-risk participants were lower. The findings of Early-BipoLife underscore the importance of considering risk factors beyond family history in order to improved early detection and interventions to prevent/ameliorate related impairment in the course of BD. Large long-term cohort studies are crucial to understand the developmental pathways and long-term course of BD, especially in people at- risk

Individuals at increased risk for development of bipolar disorder display structural alterations similar to people with manifest disease

In psychiatry, there has been a growing focus on identifying at-risk populations. For schizophrenia, these efforts have led to the development of early recognition and intervention measures. Despite a similar disease burden, the populations at risk of bipolar disorder have not been sufficiently characterized. Within the BipoLife consortium, we used magnetic resonance imaging (MRI) data from a multicenter study to assess structural gray matter alterations in N = 263 help-seeking individuals from seven study sites. We defined the risk using the EPIbipolar assessment tool as no-risk, low-risk, and high-risk and used a region-of-interest approach (ROI) based on the results of two large-scale multicenter studies of bipolar disorder by the ENIGMA working group. We detected significant differences in the thickness of the left pars opercularis (Cohen’s d = 0.47, p = 0.024) between groups. The cortex was significantly thinner in high-risk individuals compared to those in the no-risk group (p = 0.011). We detected no differences in the hippocampal volume. Exploratory analyses revealed no significant differences in other cortical or subcortical regions. The thinner cortex in help-seeking individuals at risk of bipolar disorder is in line with previous findings in patients with the established disorder and corresponds to the region of the highest effect size in the ENIGMA study of cortical alterations. Structural alterations in prefrontal cortex might be a trait marker of bipolar risk. This is the largest structural MRI study of help-seeking individuals at increased risk of bipolar disorder

Exploratory study of ultraviolet B (UVB) radiation and age of onset of bipolar disorder

International audienceBackground: Sunlight contains ultraviolet B (UVB) radiation that triggers the production of vitamin D by skin. Vitamin D has widespread effects on brain function in both developing and adult brains. However, many people live at latitudes (about > 40 N or S) that do not receive enough UVB in winter to produce vitamin D. This exploratory study investigated the association between the age of onset of bipolar I disorder and the threshold for UVB sufficient for vitamin D production in a large global sample.Methods: Data for 6972 patients with bipolar I disorder were obtained at 75 collection sites in 41 countries in both hemispheres. The best model to assess the relation between the threshold for UVB sufficient for vitamin D production and age of onset included 1 or more months below the threshold, family history of mood disorders, and birth cohort. All coefficients estimated at P ≤ 0.001.Results: The 6972 patients had an onset in 582 locations in 70 countries, with a mean age of onset of 25.6 years. Of the onset locations, 34.0% had at least 1 month below the threshold for UVB sufficient for vitamin D production. The age of onset at locations with 1 or more months of less than or equal to the threshold for UVB was 1.66 years younger.Conclusion: UVB and vitamin D may have an important influence on the development of bipolar disorder. Study limitations included a lack of data on patient vitamin D levels, lifestyles, or supplement use. More study of the impacts of UVB and vitamin D in bipolar disorder is needed to evaluate this supposition

Exploratory study of ultraviolet B (UVB) radiation and age of onset of bipolar disorder

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