Die Bedeutung des Kleinhirns bei der Adaptation von Atmung und Standmotorik an erwartete und unerwartete motorische Störungen.

Die vorliegende Studie prüfte an gesunden Probanden, ob die Reaktionen der Atmung und Standmotorik auf erwartete und unerwartete Störungen des Körpergleichgewichts im Sinne von plastischen Lernprozessen modifzierbar sind. Anhand von Patienten mit degenerativen Kleinhirnatrophien wurde untersucht, ob das Kleinhirn an der Entstehung, Adaptation und Integration derartiger Reaktionsmustern beteiligt ist. Zu Beginn der Versuche wurde die Atmung der Probanden bei ruhigem Stand aufgezeichnet. Gleichzeitig wurden die standmotorischen Reaktionen der Mm. gastocnemius und tibialis anterior, sowie die Körperschwerpunktskräfte registiert. In der ersten Versuchsanordnung wurden unerwartete,rampenförmige Plattformkippungen benützt, die innerhalb verschiedener Abschnitte des Atemzyklus appliziert wurden. Im zweiten Versuchsparadigma wurden dagegen erwartete,kontinuierlich-sinusförmige Plattformbewegungen unterschiedlicher Frequenz verwendet. Unerwartete Plattformkippungen führten bei den gesunden Probanden abhängig vom gestörten, respiratorischen Phasenabschnitt zu einer Verkürzung der Atemphasen, wobei die Exspirationsphase stärker als die Inspirationsphase betroffen war. An den Phasenübergängen war dieser modulatorische Effekt am ausgeprägtesten. Dagegen war die Variabilität der Atmung hier deutlich niedriger als bei Perturbationen innerhalb der frühen und mittleren exspiratorischen Phasenabschnitte. Im Gegensatz zur Atmung ließen sich in den Reaktionen der Muskeln und in dem aus den Kräften ermittelten Schwerpunktstrajektor keine atemphasenabhängigen Veränderungen feststellen. Erwartete Störungen des Körpergleichgewichts induzierten bei den gesunden Probanden eine individuell unterschiedlich starke Kopplung von Atem- und Plattformrhythmus. Diese Kopplung war bei Plattformfrequenzen im Bereich der Atemruhefrequenz am größten. In Richtung der unteren und oberen Grenze des untersuchten Frequenzbereichs, die beim Drittel und Dreifachen der Atemruhefrequenz lag, nahm diese Kopplung ab. Die Reaktionen des M. gastrocnemius waren v.a. bei höheren Frequenzen stärker als die des M. tibialis anterior an die Plattformbewegung gekoppelt. Wie bei der Atmung ließ sich bei den muskulären Reaktionen eine Abnahme der Kopplung in den oberen und unteren Frequenzbereichen feststellen. In allen Versuchsserien zeigten die Patienten eine im Vergleich zu den gesunden Probanden reduzierte respiratorische Anpassungsfähigkeit. Bereits in der Ruhemessung wies die Patientengruppe eine höhere Atemfrequenz bei geringeren respiratorischen Frequenzschwankungen auf. Die unerwarteten, plattforminduzierten Störungen des Körpergleichgewichts führten bei der Patientengruppe zu einer geringeren und kürzeren Modulation der Atmung. Bei den erwarteten Störungen unterschieden sich die Patienten durch eine schwächere Kopplung von Atmung und Plattformbewegung. Bei keiner der untersuchten Versuchsgruppen ließ sich eine Abhängigkeit der standmotorischen Reaktionen von den Bedingungen der Atemtätigkeit nachweisen. Die vorliegende Studie zeigte eine durch unerwartete wie erwartete motorische Störungen ausgelöste, funktionelle Hierarchisierung der atem- und standmotorischen Reaktionen, an der das Kleinhirn durch Adaptation und Integration der Atemmotorik beteiligt war

Cerebral differences in explicit and implicit emotional processing - An fMRI study

The processing of emotional facial expression is a major part of social communication and understanding. In addition to explicit processing, facial expressions are also processed rapidly and automatically in the absence of explicit awareness. We investigated 12 healthy subjects by presenting them with an implicit and explicit emotional paradigm. The subjects reacted significantly faster in implicit than in explicit trials but did not differ in their error ratio. For the implicit condition increased signals were observed in particular in the thalami, the hippocampi, the frontal inferior gyri and the right middle temporal region. The analysis of the explicit condition showed increased blood-oxygen-level-dependent signals especially in the caudate nucleus, the cingulum and the right prefrontal cortex. The direct comparison of these 2 different processes revealed increased activity for explicit trials in the inferior, superior and middle frontal gyri, the middle cingulum and left parietal regions. Additional signal increases were detected in occipital regions, the cerebellum, and the right angular and lingual gyrus. Our data partially confirm the hypothesis of different neural substrates for the processing of implicit and explicit emotional stimuli. Copyright (c) 2007 S. Karger AG, Basel

Association between formal thought disorders, neurocognition and functioning in the early stages of psychosis: a systematic review of the last half-century studies

Recent review articles provided an extensive collection of studies covering many aspects of format thought disorders (FTD) among their epidemiology and phenomenology, their neurobiological underpinnings, genetics as well as their transdiagnostic prevalence. However, less attention has been paid to the association of FTD with neurocognitive and functioning deficits in the early stages of evolving psychosis. Therefore, this systematic review aims to investigate the state of the art regarding the association between FTD, neurocognition and functioning in the early stages of evolving psychotic disorders in adolescents and young adults, by following the PRISMA flowchart. A total of 106 studies were screened. We included 8 studies due to their reports of associations between FTD measures and functioning outcomes measured with different scales and 7 studies due to their reports of associations between FTD measures and neurocognition. In summary, the main findings of the included studies for functioning outcomes showed that FTD severity predicted poor social functioning, unemployment, relapses, re-hospitalisations, whereas the main findings of the included studies for neurocognition showed correlations between attentional deficits, executive functions and FTD, and highlighted the predictive potential of executive dysfunctions for sustained FTD. Further studies in upcoming years taking advantage of the acceleration in computational psychiatry would allow researchers to re-investigate the clinical importance of FTD and their role in the transition from at-risk to full-blown psychosis conditions. Employing automated computer-assisted diagnostic tools in the early stages of psychosis might open new avenues to develop targeted neuropsychotherapeutics specific to FTD

Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model

Autism spectrum disorder (ASD) is diagnosed on the basis of speech and communication differences, amongst other symptoms. Since conversations are essential for building connections with others, it is important to understand the exact nature of differences between autistic and non-autistic verbal behaviour and evaluate the potential of these differences for diagnostics. In this study, we recorded dyadic conversations and used automated extraction of speech and interactional turn-taking features of 54 non-autistic and 26 autistic participants. The extracted speech and turn-taking parameters showed high potential as a diagnostic marker. A linear support vector machine was able to predict the dyad type with 76.2% balanced accuracy (sensitivity: 73.8%, specificity: 78.6%), suggesting that digitally assisted diagnostics could significantly enhance the current clinical diagnostic process due to their objectivity and scalability. In group comparisons on the individual and dyadic level, we found that autistic interaction partners talked slower and in a more monotonous manner than non-autistic interaction partners and that mixed dyads consisting of an autistic and a non-autistic participant had increased periods of silence, and the intensity, i.e. loudness, of their speech was more synchronous

individualized diagnostic and prognostic models for patients with psychosis risk syndromes a meta analytic view on the state of the art

Abstract Background The Clinical High Risk(CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods like machine learning(ML) and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic, i.e., distinguishing CHR from healthy individuals, and prognostic models, i.e., predicting a future outcome, based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity both of CHR populations and methodologies applied. Methods We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and ML. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality. Results Forty-four articles were included covering 3707 individuals for prognostic and 1052 for diagnostic studies(572 CHR and 480 healthy controls). CHR patients could be classified against healthy controls with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity 78%. ML models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies, yet no other moderator effects. Conclusions Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice

T179. DO INDIVIDUALS IN A CLINICAL HIGH-RISK STATE FOR PSYCHOSIS DIFFER FROM HEALTHY CONTROLS IN THEIR CORTICAL FOLDING PATTERNS?

Background Volumetric brain differences between persons meeting criteria for a clinical high-risk state for psychosis (CHR) and healthy controls (HC) have been previously reported, yet little is known about potential abnormalities in surface-based morphological measures. Gyrification (i.e., the amount of cortical convolution) remains relatively stable across the lifespan and is minimally influenced by ubiquitous confounding factors (e.g., drug use, medication, or stress). Recently, a multi-site analysis conducted in 104 CHR persons found global increases in cortical gyrification compared to HC (Sasabayashi et al. 2017). If replicated, gyrification abnormalities in CHR could potentially serve as early neuromarkers of elevated risk, and thus could eventually be used to identify objectively and efficiently the CHR state. Methods A total of 124 CHR and 264 HC subjects were recruited as part of the PRONIA consortium (www.pronia.eu), a large-scale international longitudinal study currently consisting of 10 European sites. Cortical surfaces were reconstructed from structural MRI images using a volume-based, newly introduced technique called the Projection-Based-Thickness (PBT) as available in the SPM-based-toolbox CAT12. Local gyrification was quantified automatically across the whole brain as absolute mean curvature for each vertex of the brain surface mesh consisting of thousands of individual measurement points. Vertex-wise differences of curvature values were calculated applying a General Linear Model, corrected for age, gender and site effects. Results were investigated at corrected and uncorrected levels. Results We found no significant differences in vertex-wise gyrification between CHR and HC at either corrected or uncorrected levels (p>0.05). Further investigations of potential confounding site effects also did not reveal differences. Discussion Our preliminary findings suggest that CHR subjects do not show whole-brain gyrification abnormalities when compared with healthy subjects. These negative results agree with literature suggesting that cortical convolution might be more affected by neurodevelopmental or genetic factors, and thus deviations from normal patterns might not be detectable in heterogeneous samples of at-risk subjects wherein the etiology and ultimate prognosis is unknown. In order to better investigate differences in cortical folding and address the role of gyrification as neuroanatomical biomarker for psychosis, future investigations should focus on subgroups within CHR populations (e.g. patients groups defined by basic symptoms, ultra-high risk, or familial risk) in addition to specific analyses of individuals with higher neurodevelopmental (e.g., obstetric complications) or genetic (e.g., polygenic risk) loadings

Grey-matter abnormalities in clinical high-risk participants for psychosis

The current study examined the presence of abnormalities in cortical grey-matter (GM) in a sample of clinical high-risk (CHR) participants and examined relationships with psychosocial functioning and neurocognition. CHR-participants (n = 114), participants who did not fulfil CHR-criteria (CHR-negative) (n = 39) as well as a group of healthy controls (HC) (n = 49) were recruited. CHR-status was assessed using the Comprehensive Assessment of At-Risk Mental State (CAARMS) and the Schizophrenia Proneness Interview, Adult Version (SPI-A). The Brief Assessment of Cognition in Schizophrenia Battery (BACS) as well as tests for emotion recognition, working memory and attention were administered. In addition, role and social functioning as well as premorbid adjustment were assessed. No significant differences in GM-thickness and intensity were observed in CHR-participants compared to CHR-negative and HC. Circumscribed abnormalities in GM-intensity were found in the visual and frontal cortex of CHR-participants. Moreover, small-to-moderate correlations were observed between GM-intensity and neuropsychological deficits in the CHR-group. The current data suggest that CHR-participants may not show comprehensive abnormalities in GM. We discuss the implications of these findings for the pathophysiological theories of early stage-psychosis as well as methodological issues and the impact of different recruitment strategies

Patterns of risk—Using machine learning and structural neuroimaging to identify pedophilic offenders

BackgroundChild sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA.AimTo use machine learning and MRI data to identify PO individuals.MethodsFrom a single-center male cohort of 14 PO individuals and 15 matched healthy control (HC) individuals, we acquired diffusion tensor imaging data (anisotropy, diffusivity, and fiber tracking) in literature-based regions of interest (prefrontal cortex, anterior cingulate cortex, amygdala, and corpus callosum). We trained a linear support vector machine to discriminate between PO and HC individuals using these WM microstructure data. Post hoc, we investigated the PO model decision scores with respect to sociodemographic (age, education, and IQ) and forensic characteristics (psychopathy, sexual deviance, and future risk of sexual violence) in the PO subpopulation. We assessed model specificity in an external cohort of 53 HC individuals.ResultsThe classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P5000 = 0.018) and an out-of-sample specificity to correctly identify HC individuals of 94.3%. The predictive brain pattern contained bilateral fractional anisotropy in the anterior cingulate cortex, diffusivity in the left amygdala, and structural prefrontal cortex-amygdala connectivity in both hemispheres. This brain pattern was associated with the number of previous child victims, the current stance on sexuality, and the professionally assessed risk of future sexual violent reoffending.ConclusionAberrant white matter microstructure in the prefronto-temporo-limbic circuit could be a potential neurobiological correlate for PO individuals at high-risk of reoffending with CSA. Although preliminary and exploratory at this point, our findings highlight the general potential of MRI-based biomarkers and particularly WM microstructure patterns for future CSA risk assessment and preventive efforts

Modeling Social Sensory Processing During Social Computerized Cognitive Training for Psychosis Spectrum: The Resting-State Approach

Background: Greater impairments in early sensory processing predict response to auditory computerized cognitive training (CCT) in patients with recent-onset psychosis (ROP). Little is known about neuroimaging predictors of response to social CCT, an experimental treatment that was recently shown to induce cognitive improvements in patients with psychosis. Here, we investigated whether ROP patients show interindividual differences in sensory processing change and whether different patterns of SPC are (1) related to the differential response to treatment, as indexed by gains in social cognitive neuropsychological tests and (2) associated with unique resting-state functional connectivity (rsFC). Methods: Twenty-six ROP patients completed 10 h of CCT over the period of 4–6 weeks. Subject-specific improvement in one CCT exercise targeting early sensory processing—a speeded facial Emotion Matching Task (EMT)—was studied as potential proxy for target engagement. Based on the median split of SPC from the EMT, two patient groups were created. Resting-state activity was collected at baseline, and bold time series were extracted from two major default mode network (DMN) hubs: left medial prefrontal cortex (mPFC) and left posterior cingulate cortex (PCC). Seed rsFC analysis was performed using standardized Pearson correlation matrices, generated between the average time course for each seed and each voxel in the brain. Results: Based on SPC, we distinguished improvers—i.e., participants who showed impaired performance at baseline and reached the EMT psychophysical threshold during CCT—from maintainers—i.e., those who showed intact EMT performance at baseline and sustained the EMT psychophysical threshold throughout CCT. Compared to maintainers, improvers showed an increase of rsFC at rest between PCC and left superior and medial frontal regions and the cerebellum. Compared to improvers, maintainers showed increased rsFC at baseline between PCC and superior temporal and insular regions bilaterally. Conclusions: In ROP patients with an increase of connectivity at rest in the default mode network, social CCT is still able to induce sensory processing changes that however do not translate into social cognitive gains. Future studies should investigate if impairments in short-term synaptic plasticity are responsible for this lack of response and can be remediated by pharmacological augmentation during CCT

Acquisition and Use of 'Priors' in Autism: Typical in Deciding Where to Look, Atypical in Deciding What Is There

Individuals with Autism Spectrum Disorder (ASD) are thought to under-rely on prior knowledge in perceptual decision-making. This study examined whether this applies to decisions of attention allocation, of relevance for 'predictive-coding' accounts of ASD. In a visual search task, a salient but task-irrelevant distractor appeared with higher probability in one display half. Individuals with ASD learned to avoid 'attentional capture' by distractors in the probable region as effectively as control participants-indicating typical priors for deploying attention. However, capture by a 'surprising' distractor at an unlikely location led to greatly slowed identification of a subsequent target at that location-indicating that individuals with ASD attempt to control surprise (unexpected attentional capture) by over-regulating parameters in post-selective decision-making