Spatiotemporal dynamics of covert versus overt processing of happy, fearful and sad facial expressions

Behavioral and electrophysiological correlates of the influence of task demands on the processing of happy, sad, and fearful expressions were investigated in a within-subjects study that compared a perceptual distraction condition with task-irrelevant faces (e.g., covert emotion task) to an emotion task-relevant categorization condition (e.g., overt emotion task). A state-of-the-art non-parametric mass univariate analysis method was used to address the limitations of previous studies. Behaviorally, participants responded faster to overtly categorized happy faces and were slower and less accurate to categorize sad and fearful faces; there were no behavioral differences in the covert task. Event-related potential (ERP) responses to the emotional expressions included the N170 (140-180 ms), which was enhanced by emotion irrespective of task, with happy and sad expressions eliciting greater amplitudes than neutral expressions. EPN (200-400 ms) amplitude was modulated by task, with greater voltages in the overt condition, and by emotion, however, there was no interaction of emotion and task. ERP activity was modulated by emotion as a function of task only at a late processing stage, which included the LPP (500-800 ms), with fearful and sad faces showing greater amplitude enhancements than happy faces. This study reveals that affective content does not necessarily require attention in the early stages of face processing, supporting recent evidence that the core and extended parts of the face processing system act in parallel, rather than serially. The role of voluntary attention starts at an intermediate stage, and fully modulates the response to emotional content in the final stage of processing

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Subcortical brain volume, regional cortical thickness, and cortical surface area across disorders: findings from the ENIGMA ADHD, ASD, and OCD Working Groups

Objective Attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. We aimed to directly compare all three disorders. The ENIGMA consortium is ideally positioned to investigate structural brain alterations across these disorders. Methods Structural T1-weighted whole-brain MRI of controls (n=5,827) and patients with ADHD (n=2,271), ASD (n=1,777), and OCD (n=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. We examined subcortical volume, cortical thickness and surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults using linear mixed-effects models adjusting for age, sex and site (and ICV for subcortical and surface area measures). Results We found no shared alterations among all three disorders, while shared alterations between any two disorders did not survive multiple comparisons correction. Children with ADHD compared to those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller ICV than controls and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared to adult controls and other clinical groups. No OCD-specific alterations across different age-groups and surface area alterations among all disorders in childhood and adulthood were observed. Conclusion Our findings suggest robust but subtle alterations across different age-groups among ADHD, ASD, and OCD. ADHD-specific ICV and hippocampal alterations in children and adolescents, and ASD-specific cortical thickness alterations in the frontal cortex in adults support previous work emphasizing neurodevelopmental alterations in these disorders

Fire Alarm Signal Recognition

this report, shows the spectrum analysis of the 6 signals. 4.3 Materials The signals were recorded so that they played at a maximum of 90 dBA when the CD player was at maximum volume. This volume level is not considered harmful to the hearing of the participant if the duration of the exposure is limited to 15 minutes or less and is not repeated day after day over a long period of time (WHO, 1999). The interviewer adjusted the volume of the CD player to a comfortable level for the environment they were in at the time of the study before beginning the test (this volume was usually at 4 out of a maximum volume of 10). Each participant, however, had the opportunity to adjust the volume during a 10-second period of music, prior to listening to the test signals. The signals were played on a CD Walkman (Sony Sports model D-SJ01) and both the experimenter and participant had a set of headphones to listen to the signals (the experimenter had Sony MDR-GO51 headphones). The participant listened to the signals through Sony Noise Cancelling headphones (Sony MDR-NC20) so that the ambient noise of the environment did not overly interfere with the quality of the test signals. Three experimenters collected the data. All experimenters followed a rehearsed script so that the participants experienced a similar situation with each experimenter. The experimenters recorded the participants' answers on a data sheet that the participant could view at the time of the survey. 10 The answers to the perceived urgency rating were recorded on a simple 1-10 scale, although the psycho-acoustic literature lately suggests using a free-modulus magnitude estimation scale or the cross modality matching (Hellier et al., 1995). It was decided that these methods would not work well for the present study. Thes..

Fire Alarm Signal Recognition

The 1995 National Building Code of Canada requires that fire alarm signals sound the Temporal-Three (T-3) pattern, as defined by the ISO 8201 i2Acoustics ? Audible Emergency Evacuation Signalli. This sound pattern has also been required by NFPA 72 since July 1996. It is intended that the T-3 pattern will become the standardized alarm signal heard around the world that will unequivocally mean ieevacuate the building immediatelyly. Although new and refurbished buildings have, for the past 5 years, been equipped with this new signal, no formal public education has taken place to inform building users about the meaning and response expected from them when it sounds. In North America, discussions are ongoing regarding thenecessity to develop a public education campaign on the subject of this new evacuation signal, and whether an automatic recorded message should follow the signal to prompt the public to evacuate. As a first step, we need to ascertain if the public already recognizes this sound as an evacuation signal.Le Code national du b\ue2timent du Canada 1995 exige que les signaux \ue9mis par les alarmes d?incendie retentissent selon le mode de signalisation Temporal-3 (T-3), soit \ue0 trois (3) pulsations, comme d\ue9fini par la norme ISO 8201, \uab Acoustique ? signal sonore d?\ue9vacuation d?urgence \ubb. Le mode T-3 est \ue9galement requis depuis juillet 1996 par la norme NFPA 72. Ce mode pourrait devenir le signal d'alarme standard utilis\ue9 dans le monde entier pour signifier, sans \ue9quivoque, d?\ue9vacuer l'\ue9difice imm\ue9diatement. Bien que les \ue9difices construits ou r\ue9nov\ue9s depuis 5 ans aient tous \ue9t\ue9 \ue9quip\ue9s de ce syst\ue8me utilisant ce nouveau signal, nulle forme d?\ue9ducation officielle du public n?a jusqu?ici \ue9t\ue9 mise en place afin d?informer les usagers des b\ue2timents quant \ue0 la signification de ce signal et \ue0 la r\ue9ponse attendue de leur part lorsqu?il retentit. En Am\ue9rique du Nord, on d\ue9bat actuellement de la n\ue9cessit\ue9 de mettre sur pied une campagne d?\ue9ducation du grand public portant sur ce nouveau signal d?\ue9vacuation, et de la pertinence de le faire suivre ou non d?un message enregistr\ue9 automatique sollicitant l?\ue9vacuation des lieux. Or, comme premi\ue8re \ue9tape, il nous est n\ue9cessaire de d\ue9terminer si le public reconna\ueet d\ue9j\ue0 ce timbre comme signal d?\ue9vacuation.Peer reviewed: NoNRC publication: Ye

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: Medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Mapping cortical and subcortical asymmetry in obsessive-compulsive disorder: findings from the ENIGMA consortium

Accepted ManuscriptBACKGROUND: Lateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.METHODS: We studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.RESULTS: In the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -20.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.CONCLUSIONS: The results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.This research was funded by the Max Planck Society (Germany). Additional funding was from the Japan Society for the Promotion of Science (KAKENHI Grant No. 18K15523 [to YA], KAKENHI Grant No. 16K04344 [to YH], KAKENHI Grant Nos. 16K19778 and 18K07608 [to TNakam], and KAKENHI Grant No. 26461762 [to AN]); the Carlos III Health Institute (Grant No. PI14/00419 [to PA], Grant No. PI040829 cofunded by European Regional Development Fund [to LL], Grant No. FI17/00294 [to IM-Z], Grant No. PI16/00950 [to JMM], and Grant Nos. CPII16/00048, PI13/01958, and PI16/00889 cofunded by European Regional Development Funds [to CS-M]); the Ontario Mental Health Foundation (Research Training Fellowship [to SHA]); Alberta Innovates Translational Health Chair in Child and Youth Mental Health (to PDA), the Ontario Brain Institute (to PDA); the National Institute of Mental Health (Grant No. K23MH104515 [to JTB], Grant No. K23-MH092397 [to BPB], Grant No. K23MH082176 [to KDF), Grant No. R21MH101441 [to RM], Grant No. R01MH081864 [to JO and JP], and Grant No. R01MH085900 [to JO and JF], Grant No. R21MH093889 [to HBS]); Fundação de Amparo à Pesquisa do Estado de São Paulo (Grant No. 2011/21357–9 [to MCB], Grant No. 2011/21357–9 [to GFB], Grant No. 2011/21357–9 [to MQH], and Grant No. 2011/21357–9 [to ECM]); the Swiss National Science Foundation (Grant No. 320030_130237 [to SB; principal investigator, Susanne Walitza]); the Hartmann Müller Foundation (Grant No. 1460 [to SB]); the David Judah Fund at the Massachusetts General Hospital (to BPB); EU FP7 Project TACTICS (Grant No. 278948 [to JB]); the National Natural Science Foundation of China (Grant No. 81560233 [to YC] and Grant No. 81371340 [to ZW]); the International OCD Foundation (Grant No. K23 MH115206 [to PG]); the Wellcome Sir Henry Dale Fellowship (Grant No. 211155/Z/18/Z [to TUH]); the Jacobs Foundation (to TUH); the Brain and Behavior Research Foundation (2018 NARSAD Young Investigator Grant No. 27023 [to TUH]); the Agency for Medical Research and Development (Grant No. JP18dm0307002 [to YH]); the Michael Smith Foundation for Health Research (to FJ-F); the Federal Ministry of Education and Research of Germany (Grant No. BMBF-01GW0724 [to NK]); the Deutsche Forschungsgemeinschaft (Grant No. KO 3744/7–1 [to KK]); the Helse Vest Health Authority (Grant Nos. 911754 and 911880 [to GK]); the Norwegian Research Council (Grant No. HELSEFORSK 243675 [to GK]); the Marató TV3 Foundation (Grant Nos. 01/2010 and 091710 [to LL]); the Agency for Management of University and Research Grants (Grant No. 2017 SGR 881 [to LL] and 2017 SGR 1247 from the Generalitat de Catalunya [to JMM]); Fundação para a Ciência e a Tecnologia (Grant No. PDE/BDE/113604/2015 from the PhD-iHES Program [to RM], Grant No. PDE/BDE/113601/2015 from the PhD-iHES Program [to PSM]); the Japanese Ministry of Education, Culture, Sports, Science and Technology (Grant-in-Aid for Scientific Research (Grant Nos. 22591262, 25461732, and 16K10253 [to TNakao]); the Government of India Department of Science and Technology (DST INSPIRE Faculty Grant No. -IFA12-LSBM-26 [to JCN] and Grant No. SR/S0/HS/0016/2011 [to YCJR]); the Government of India Department of Biotechnology (Grant No. BT/06/IYBA/2012 [to JCN] and Grant No. BT/PR13334/Med/30/259/2009 [to YCJR]); the New York State Office of Mental Health (to HBS); the Italian Ministry of Health (Grant No. RC13-14-15-16A [to GS]); the National Center for Advancing Translational Sciences (Grant No. UL1TR000067/KL2TR00069 [to ERS]); the Canadian Institutes of Health Research (to SES); the Michael Smith Foundation for Health Research (to SES); the British Columbia Provincial Health Services Authority (to SES); the Netherlands Organization for Scientific Research (Grant No. NWO/ZonMW Vidi 917.15.318 [to GAvW]); the Wellcome-DBT India Alliance (Grant No. 500236/Z/11/Z [to GV]); the Shanghai Key Laboratory of Psychotic Disorders (Grant No. 13dz2260500 [to ZW])

Mapping Cortical and Subcortical Asymmetry in Obsessive-Compulsive Disorder: Findings From the ENIGMA Consortium

BACKGROUND: Lateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD. METHODS: We studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status. RESULTS: In the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -20.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets. CONCLUSIONS: The results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.N

Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups

ObjectiveAttention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. The authors sought to directly compare these disorders using structural brain imaging data from ENIGMA consortium data.MethodsStructural T1-weighted whole-brain MRI data from healthy control subjects (N=5,827) and from patients with ADHD (N=2,271), ASD (N=1,777), and OCD (N=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. The authors examined subcortical volume, cortical thickness, and cortical surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults, using linear mixed-effects models adjusting for age, sex, and site (and intracranial volume for subcortical and surface area measures).ResultsNo shared differences were found among all three disorders, and shared differences between any two disorders did not survive correction for multiple comparisons. Children with ADHD compared with those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller intracranial volume than control subjects and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared with adult control subjects and other clinical groups. No OCD-specific differences were observed across different age groups and surface area differences among all disorders in childhood and adulthood.ConclusionsThe study findings suggest robust but subtle differences across different age groups among ADHD, ASD, and OCD. ADHD-specific intracranial volume and hippocampal differences in children and adolescents, and ASD-specific cortical thickness differences in the frontal cortex in adults, support previous work emphasizing structural brain differences in these disorders