Longterm course of neuropsychological symptoms and ME/CFS after SARS-CoV-2-infection: a prospective registry study

A significant proportion of patients after SARS-CoV-2 infection suffer from long-lasting symptoms. Although many different symptoms are described, the majority of patients complains about neuropsychological symptoms. Additionally, a subgroup of patients fulfills diagnostic criteria for ME/CFS. We analyzed a registry of all patients presenting in the out-patients clinic at a German university center. For patients with more than one visit, changes in reported symptoms from first to second visit were analyzed. A total of 1022 patients were included in the study, 411 of them had more than one visit. 95.5% of the patients reported a polysymptomatic disease. At the first visit 31.3% of the patients fulfilled ME/CFS criteria after a median time of 255 days post infection and and at the second visit after a median of 402 days, 19.4% still suffered from ME/CFS. Self-reported fatigue (83.7–72.7%) and concentration impairment (66.2–57.9%) decreased from first to second visit contrasting non-significant changes in the structured screening. A significant proportion of SARS-CoV-2 survivors presenting with ongoing symptoms present with ME/CFS. Although the proportion of subjective reported symptoms and their severity reduce over time, a significant proportion of patients suffer from long-lasting symptoms necessitating new therapeutic concepts

Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12–68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = −0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = −0.690, pspin = 0.006), BD (rho = −0.672, pspin = 0.009), and MDD (rho = −0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype

Transdiagnostic alterations in white matter microstructure associated with suicidal thoughts and behaviours in the ENIGMA Suicidal Thoughts and Behaviours consortium

Previous studies have suggested that alterations in white matter (WM) microstructure are implicated in suicidal thoughts and behaviours (STBs). However, findings of diffusion tensor imaging (DTI) studies have been inconsistent. In this large-scale mega-analysis conducted by the ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium, we examined WM alterations associated with STBs. Data processing was standardised across sites, and resulting WM microstructure measures (fractional anisotropy, axial diffusivity, mean diffusivity and radial diffusivity) for 25 WM tracts were pooled across 40 cohorts. We compared these measures among individuals with a psychiatric diagnosis and lifetime history of suicide attempt ( n=652; mean age=35.4±14.7; female=71.8%), individuals with a psychiatric diagnosis but no STB (i.e., clinical controls; n=1871; mean age=34±14.8; female=59.8%), and individuals with no mental disorder diagnosis and no STB (i.e., healthy controls; n=642; mean age=29.6±13.1; female=62.9%). We also compared these measures among individuals with recent suicidal ideation ( n=714; mean age=36.3±15.3; female=66.1%), clinical controls ( n=1184; mean age=36.8±15.6; female=63.1%), and healthy controls ( n=1240; mean age= 31.6±15.5; female=61.0%). We found subtle but statistically significant effects, such as lower fractional anisotropy associated with a history of suicide attempt, over and above the effect of psychiatric diagnoses. These effects were strongest in the corona radiata, thalamic radiation, fornix/stria terminalis, corpus callosum and superior longitudinal fasciculus. Effect sizes were small (Cohen's d &lt; 0.25). Recent suicidal ideation was not associated with alterations in WM microstructure. This large-scale coordinated mega-analysis revealed subtle regional and global alterations in WM microstructure in individuals with a history of suicide attempt. Longitudinal studies are needed to confirm whether these alterations are a risk factor for suicidal behaviour. </p

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

Twin studies of brain structure and cognition in schizophrenia

Twin studies in schizophrenia have been crucial in establishing estimates for the heritability and thus providing evidence for a genetic component in this disorder. Recent years have seen the application of the twin study paradigm to both putative intermediate phenotypes and biomarkers of disease as well as a diversification of its use in schizophrenia research. This review addressed studies of brain structure (T1 morphometry) and cognition in schizophrenia using twin study designs. We review major findings such as the overlap of genetic variance between schizophrenia and cognition as a model for the emergence of psychopathology. The use of novel hybrid models integrating molecular genetic risk markers, as well as the use of twin studies in epigenetics might prove to significantly enhance schizophrenia research in the post-GWAS era

The neural basis of hostility-related dimensions in schizophrenia

Hostility and related dimensions like anger, urgency, impulsivity and aggressiveness have been described in non-clinical populations and various serious mental illnesses including schizophrenia. Although representing a mental healthcare challenge, the investigation of such constructs is often limited by the presence of complex and multi-factorial causes and lack of agreement in their conceptualisation and measurement. In this review, we aim to clarify the anatomical basis of hostility-related dimensions in schizophrenia. Imaging studies suggest malfunctioning of a neural circuitry including amygdala, striatum, prefrontal cortex, anterior cingulate cortex, insula and hippocampus to modulate hostile thoughts and behaviours, at least in the subgroup of patients with schizophrenia who exhibit high levels of urgency, impulsivity and aggressiveness

Hirnstrukturelle Veränderungen bei chronischem, beidseitigem Tinnitus: Subtypen und Komorbidität

Chronischer Tinnitus ist mit hirnstrukturellen Veränderungen assoziiert, sowohl mit Reduktionen im auditorischen System als auch in limbischen Arealen. Es ist unklar, ob letztere mit der starken emotionalen Bewertung oder weiteren häufigen Belastungsfaktoren der Patienten assoziiert sind. In einer laufenden Studie untersuchen wir diese Hypothese mit MR-Morphometrie in einem Design aus Subgruppen chronischer Tinnitus-Patienten und Kontrollgruppen mit bzw. ohne psychischer Belastung.Aus der laufenden Studie schlossen wir für die vorliegende Analyse 24 Patienten mit chronischem bilateralen Tinnitus ein, von denen anhand des Tinnitus-Fragebogens (Goebel und Hiller) 13 als kompensiert und 11 als dekompensiert eingestuft und mit 20 gesunden Kontrollen verglichen wurden. Hochauflösende T1-cMRT dieser Probanden wurden mittels voxel-basierter Morphometrie analysiert.Wir fanden eine signifikante Verminderung des corticalen Volumens bei dekompensierten ggü. kompensierten Tinnitus-Patienten und gesunden Kontrollen im inferioren occipitalen Gyrus rechts. Weitere Befunde ergaben sich im Pallidum (kompensiert gesunde Kontrollen).Unsere vorläufige Analyse zeigt hirnstrukturelle Defizite, welche sich zwischen Subtypen von Tinnitus-Patienten unterscheiden. Variationen in diesem Muster könnten neben der unterschiedlichen klinischen Ausprägung der Symptomatik auch die Komorbidität mit psychischen Störungen erklären. In der laufenden Studie untersuchen wir daher auch eine hörgesunde psychiatrische Kontrollgruppe, um in einem erweiterten Sample die Faktoren Tinnitus versus psychische Belastung weiter zu differenzieren.Unterstützt durch: IZKF Jena (B. Besteher)Der Erstautor gibt keinen Interessenkonflikt an

Corticale Veränderungen im Temporalpol bei Anosmikern und Hyposmikern mit depressiven Symptomen - Schnittstelle zwischen Geruchssinn und Emotion?

Patienten mit chronischer Anosmie oder Hyposmie zeigen diskrete hirnstrukturelle Veränderungen in sekundär-sensorischen olfaktorischen aber auch limbischen Arealen (Bitter et al., 2010a,b). Diese Patienten haben vermehrt depressive Symptome, während umgekehrt Patienten mit Major Depression Defizite des Geruchsempfindens zeigen, welche sich unter antidepressiver Behandlung zurückbilden. Ausgehend von diesen Vorbefunden untersuchten wir mittels voxel-basierter Morphometrie, einem automatisierten Verfahren zur Analyse hochauflösender T1-cMRT, die Korrelation von depressiven Symptomen und Hirnstruktur bei 31 Patienten mit Riechminderung seit über 6m (ermittelt durch Sniffin´ Sticks Test, mittlerer SDI 21,1 Punkte, SD 2,1), deren Stimmung mittels Becks Depressionsinventar (BDI) erfasst wurde (mittlerer BDI 3,3, SD 3,3). Das corticale Volumen wurden mittels CAT 12 toolbox (basierend auf SPM12) unter Anwendung eines allgemeinen linearen Modells mit den BDI-Werten korreliert.Wir fanden eine signifikante negative Korrelation des corticalen Volumen im rechten Temporalpol mit dem BDI-Score (p < 0.05, FWE-korrigiert auf cluster-level).Der Temporalpol ist ein komplexes Areal, das funktionell neuroanatomisch sowohl olfaktorische Sinneseindrücke als auch emotionale Prozesse verarbeitet und miteinander verknüpft. Defizite in diesem Areal zeigen sich auch bei ersterkrankten depressiven Patienten. Unsere Befunde legen nahe, dass die Variation der grauen Substanz im Temporalpol die Assoziation zwischen Geruchsminderung und emotionaler Beeinträchtigung vermitteln könnte. Weitere Studien mit größeren Fallzahlen sind notwendig, um diese wichtige Schnittstelle zwischen Geruchssinn und Stimmung genauer zu untersuchen.Der Erstautor gibt keinen Interessenkonflikt an