Grey matter volume and CSF biomarkers predict neuropsychological subtypes of MCI.

There is increasing evidence of different subtypes of individuals with mild cognitive impairment (MCI). An important line of research is whether neuropsychologically-defined subtypes have distinct patterns of neurodegeneration and cerebrospinal fluid (CSF) biomarker composition. In our study, we demonstrated that MCI participants of the ADNI database (N = 640) can be discriminated into 3 coherent neuropsychological subgroups. Our clustering approach revealed amnestic MCI, mixed MCI, and cluster-derived normal subgroups. Furthermore, classification modeling revealed that specific predictive features can be used to differentiate amnestic and mixed MCI from cognitively normal (CN) controls: CSF Aß142 concentration for the former and CSF Aß1-42 concentration, tau concentration as well as grey matter atrophy (especially in the temporal and occipital lobes) for the latter. In contrast, participants from the cluster-derived normal subgroup exhibited an identical profile to CN controls in terms of cognitive performance, brain structure, and CSF biomarker levels. Our comprehensive data analytics strategy provides further evidence that multimodal neuropsychological subtyping is both clinically and neurobiologically meaningful.

Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity.

BACKGROUND: Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors. OBJECTIVE: Here, we bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants. METHODS: In both brain assessments, we used penalized logistic regression models and nonparametric permutation. RESULTS: We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings. CONCLUSIONS: These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.

Phenotypical predictors of pregnancy-related restless legs syndrome and their association with basal ganglia and the limbic circuits.

Restless legs syndrome (RLS) in pregnancy is a common disorder with a multifactorial etiology. A neurological and obstetrical cohort of 308 postpartum women was screened for RLS within 1 to 6 days of childbirth and 12 weeks postpartum. Of the 308 young mothers, 57 (prevalence rate 19%) were identified as having been affected by RLS symptoms in the recently completed pregnancy. Structural and functional MRI was obtained from 25 of these 57 participants. A multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors of RLS. A decreased volume of the parietal, orbitofrontal and frontal areas shortly after delivery was found to be linked to persistent RLS symptoms up to 12 weeks postpartum, the symptoms' severity and intensity in the most recent pregnancy, and a history of RLS in previous pregnancies. The same negative relationship was observed between brain volume and not being married, not receiving any iron supplement and higher numbers of stressful life events. High cortisol levels, being married and receiving iron supplements, on the other hand, were found to be associated with increased volumes in the bilateral striatum. Investigating RLS symptoms in pregnancy within a brain-phenotype framework may help shed light on the heterogeneity of the condition.

Behavior Score-Embedded Brain Encoder Network for Improved Classification of Alzheimer Disease Using Resting State fMRI.

The ability to accurately detect onset of dementia is important in the treatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients are based on an integrated assessment of psychological tests and brain imaging such as positron emission tomography (PET) and anatomical magnetic resonance imaging (MRI). In this work using two different datasets, we propose a behavior score-embedded encoder network (BSEN) that integrates regularly adminstrated psychological tests information into the encoding procedure of representing subject's resting-state fMRI data for automatic classification tasks. BSEN is based on a 3D convolutional autoencoder structure with contrastive loss jointly optimized using behavior scores from Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). Our proposed classification framework of using BSEN achieved an overall recognition accuracy of 59.44% (3-class classification: AD, MCI and Healthy Control), and we further extracted the most discriminative regions between healthy control (HC) and AD patients.

Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments.

We simultaneously revisited the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) with a comprehensive data-analytics strategy. Here, the combination of pattern-analysis algorithms and extensive data resources (n = 266 patients aged 7-49 years) allowed identifying coherent clinical constellations in and across ADI-R and ADOS assessments widespread in clinical practice. Our clustering approach revealed low- and high-severity patient groups, as well as a group scoring high only in the ADI-R domains, providing quantitative contours for the widely assumed autism subtypes. Sparse regression approaches uncovered the most clinically predictive questionnaire domains. The social and communication domains of the ADI-R showed convincing performance to predict the patients' symptom severity. Finally, we explored the relative importance of each of the ADI-R and ADOS domains conditioning on age, sex, and fluid IQ in our sample. The collective results suggest that (i) identifying autism subtypes and severity for a given individual may be most manifested in the ADI-R social and communication domains, (ii) the ADI-R might be a more appropriate tool to accurately capture symptom severity, and (iii) the ADOS domains were more relevant than the ADI-R domains to capture sex differences.

Patterns of schizophrenia symptoms: hidden structure in the PANSS questionnaire.

The clinical presentation of patients with schizophrenia has long been described to be very heterogeneous. Coherent symptom profiles can probably be directly derived from behavioral manifestations quantified in medical questionnaires. The combination of machine learning algorithms and an international multi-site dataset (n = 218 patients) identified distinctive patterns underlying schizophrenia from the widespread PANSS questionnaire. Our clustering approach revealed a negative symptom patient group as well as a moderate and a severe group, giving further support for the existence of schizophrenia subtypes. Additionally, emerging regression analyses uncovered the most clinically predictive questionnaire items. Small subsets of PANSS items showed convincing forecasting performance in single patients. These item subsets encompassed the entire symptom spectrum confirming that the different facets of schizophrenia can be shown to enable improved clinical diagnosis and medical action in patients. Finally, we did not find evidence for complicated relationships among the PANSS items in our sample. Our collective results suggest that identifying best treatment for a given individual may be grounded in subtle item combinations that transcend the long-trusted positive, negative, and cognitive categories.

Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function.

Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal-directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyperconnectivity and hypoconnectivity within the DMN, and both increased and decreased DMN coupling with the multimodal saliency network (SN) and dorsal attention network (DAN). This study systematically revisited network disruption in patients with schizophrenia using data-derived network atlases and multivariate pattern-learning algorithms in a multisite dataset (n = 325). Resting-state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co-occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network coupling were used to characterize healthy participants and patients with schizophrenia, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN-DAN coupling, while structural covariation results highlighted aberrant DMN-SN coupling. Our findings reframe the role of the DMN core and its relation to canonical networks in schizophrenia. We thus underline the importance of large-scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients.

Attitudes of the autism community to early autism research.

Investigation into the earliest signs of autism in infants has become a significant sub-field of autism research. This work invokes specific ethical concerns such as use of 'at-risk' language, communicating study findings to parents and the future perspective of enrolled infants when they reach adulthood. This study aimed to ground this research field in an understanding of the perspectives of members of the autism community. Following focus groups to identify topics, an online survey was distributed to autistic adults, parents of children with autism and practitioners in health and education settings across 11 European countries. Survey respondents (n = 2317) were positively disposed towards early autism research, and there was significant overlap in their priorities for the field and preferred language to describe infant research participants. However, there were also differences including overall less favourable endorsement of early autism research by autistic adults relative to other groups and a dislike of the phrase 'at-risk' to describe infant participants, in all groups except healthcare practitioners. The findings overall indicate that the autism community in Europe is supportive of early autism research. Researchers should endeavour to maintain this by continuing to take community perspectives into account.