Distinct multimodal biological and functional profiles of symptom-based subgroups in recent-onset psychosis.

Symptom heterogeneity characterizes psychotic disorders and hinders the delineation of underlying biomarkers. Here, we identify symptom-based subtypes of recent-onset psychosis (ROP) patients from the multi-center PRONIA (Personalized Prognostic Tools for Early Psychosis Management) database and explore their multimodal biological and functional signatures. We clustered N = 328 ROP patients based on their maximum factor scores in an exploratory factor analysis on the Positive and Negative Syndrome Scale items. We assessed inter-subgroup differences and compared to N = 464 healthy control (HC) individuals regarding gray matter volume (GMV), neurocognition, polygenic risk scores, and longitudinal functioning trajectories. Finally, we evaluated factor stability at 9- and 18-month follow-ups. A 4-factor solution optimally explained symptom heterogeneity, showing moderate longitudinal stability. The ROP-MOTCOG (Motor/Cognition) subgroup was characterized by GMV reductions within salience, control and default mode networks, predominantly throughout cingulate regions, relative to HC individuals, had the most impaired neurocognition and the highest genetic liability for schizophrenia. ROP-SOCWD (Social Withdrawal) patients showed GMV reductions within medial fronto-temporal regions of the control, default mode, and salience networks, and had the lowest social functioning across time points. ROP-POS (Positive) evidenced GMV decreases in salience, limbic and frontal regions of the control and default mode networks. The ROP-AFF (Affective) subgroup showed GMV reductions in the salience, limbic, and posterior default-mode and control networks, thalamus and cerebellum. GMV reductions in fronto-temporal regions of the salience and control networks were shared across subgroups. Our results highlight the existence of behavioral subgroups with distinct neurobiological and functional profiles in early psychosis, emphasizing the need for refined symptom-based diagnosis and prognosis frameworks.

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS: Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS: Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS: These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach.

BACKGROUND: Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach. METHODS: In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs). RESULTS: At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features. CONCLUSIONS: The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.

Individual prediction of trauma-focused psychotherapy response in youth with posttraumatic stress disorder using resting-state functional connectivity.

Randomized controlled trials have shown efficacy of trauma-focused psychotherapies in youth with posttraumatic stress disorder (PTSD). However, response varies considerably among individuals. Currently, no biomarkers are available to assist clinicians in identifying youth who are most likely to benefit from treatment. In this study, we investigated whether resting-state functional magnetic resonance imaging (rs-fMRI) could distinguish between responders and non-responders on the group- and individual patient level. Pre-treatment rs-fMRI was recorded in 40 youth (ages 8-17 years) with (partial) PTSD before trauma-focused psychotherapy. Change in symptom severity from pre- to post-treatment was assessed using the Clinician-Administered PTSD scale for Children and Adolescents to divide participants into responders (≥30% symptom reduction) and non-responders. Functional networks were identified using meta-independent component analysis. Group-differences within- and between-network connectivity between responders and non-responders were tested using permutation testing. Individual predictions were made using multivariate, cross-validated support vector machine classification. A network centered on the bilateral superior temporal gyrus predicted treatment response for individual patients with 76% accuracy (pFWE = 0.02, 87% sensitivity, 65% specificity, area-under-receiver-operator-curve of 0.82). Functional connectivity between the frontoparietal and sensorimotor network was significantly stronger in non-responders (t = 5.35, pFWE = 0.01) on the group-level. Within-network connectivity was not significantly different between groups. This study provides proof-of-concept evidence for the feasibility to predict trauma-focused psychotherapy response in youth with PTSD at an individual-level. Future studies are required to test if larger cohorts could increase accuracy and to test further generalizability of the prediction models.

Dysregulated functional brain connectivity in response to acute social-evaluative stress in adolescents with PTSD symptoms.

Background: Posttraumatic stress disorder (PTSD) is associated with dysregulated neural, cortisol, and cardiac stress reactivity and recovery. This understanding is predominantly based on studies in adults applying emotional-cognitive and trauma-related stimuli inducing negative emotions or perceived threat. Despite large numbers of adolescents with PTSD, few studies are available on neurobiological stress reactivity in this population. Moreover, no previous studies investigated neural reactivity to social-evaluative stress. Objective: To investigate functional brain connectivity, cortisol and cardiac reactivity to acute social-evaluative stress, and additional cortisol measures in trauma-exposed adolescents with and without high PTSD symptoms. Method: A speech preparation task to induce acute social-evaluative stress elicited by anticipatory threat, was used in a subsample of the Amsterdam Born Child and their Development (ABCD) birth cohort, consisting of trauma-exposed adolescents with (n = 20) and without (n = 29) high PTSD symptoms. Psychophysiological interaction analyses were performed to assess group differences in functional connectivity of the hippocampus, mPFC and amygdala during social-evaluative stress and recovery, measured by fMRI. Additionally, perceived stress, heart rate and cortisol stress reactivity and recovery, cortisol awakening response and day curve were compared. Results: The stressor evoked significant changes in heart rate and perceived stress, but not cortisol. The PTSD symptom and control groups differed in functional connectivity between the hippocampus and cerebellum, middle and inferior frontal gyrus, and the mPFC and inferior frontal gyrus during social-evaluative stress versus baseline. Mostly, the same patterns were found during recovery versus baseline. We observed no significant group differences in amygdala connectivity, and cortisol and cardiac measures. Conclusions: Our findings suggest threat processing in response to social-evaluative stress is disrupted in adolescents with PTSD symptoms. Our findings are mainly but not entirely in line with findings in adults with PTSD, which denotes the importance to investigate adolescents with PTSD as a separate population.

Antecedentes: El trastorno de estrés postraumático (TEPT) está asociado con la recuperación. Esta comprensión se basa predominantemente en estudios con adultos, que aplican estímulos emocional-cognitivos y relacionados con el trauma que inducen emociones negativas, o percepción de amenaza. A pesar del gran número de adolescentes con TEPT, hay pocos estudios disponibles sobre la reactividad neurobiológica al estrés en esta población. Además, ningún estudio previo ha investigado la reactividad neuronal al estrés socio-evaluativo.Objetivos: Investigar la conectividad cerebral funcional, el cortisol y la reactividad cardíaca al estrés socio-evaluativo, y medidas adicionales de cortisol en adolescentes expuestos a trauma con y sin síntomas elevados de TEPT.Método: Se utilizó una tarea de preparación de discurso para inducir un estrés socio-evaluativo agudo, provocado por la amenaza anticipatoria, en una submuestra del cohorte de nacimiento del Niño nacido en Amsterdam y su Desarrollo (Amsterdam Born Child and their Development, ABCD), que consta de adolescentes expuestos a traumas con (n = 20) y sin (n = 29) síntomas elevados de TEPT. Se realizaron análisis de interacción psicofisiológica para evaluar las diferencias de grupo en la conectividad funcional del hipocampo, mPFC y amígdala durante el estrés socio-evaluativo y la recuperación, medido por fMRI. Además, se compararon el estrés percibido, la frecuencia cardíaca y la reactividad y recuperación del estrés por cortisol, la respuesta del cortisol al despertar, y la curva diurna.Resultados: El estresor provocó cambios significativos en la frecuencia cardíaca y el estrés percibido, pero no del cortisol. Los grupos con síntomas de TEPT y control difirieron en la conectividad funcional entre el hipocampo y el cerebelo, la circunvolución frontal media e inferior, y la mPFC y la circunvolución frontal inferior durante el estrés socio-evaluativo frente al valor inicial. En su mayoría, se encontraron los mismos patrones durante la recuperación frente a la línea de base. No observamos diferencias significativas entre grupos respecto de la conectividad de la amígdala, ni en las medidas de cortisol y cardíacas.Conclusiones: Nuestros hallazgos sugieren que el procesamiento de amenazas en respuesta al estrés socio-evaluativo se encuentra alterado en adolescentes con síntomas de TEPT. Nuestros hallazgos están principalmente, pero no completamente, en línea con los hallazgos en adultos con TEPT, lo que denota la importancia de investigar a adolescentes con TEPT como una población aparte.

Deep brain stimulation response in obsessive-compulsive disorder is associated with preoperative nucleus accumbens volume.

BACKGROUND: Deep brain stimulation (DBS) is a new treatment option for patients with therapy-resistant obsessive-compulsive disorder (OCD). Approximately 60% of patients benefit from DBS, which might be improved if a biomarker could identify patients who are likely to respond. Therefore, we evaluated the use of preoperative structural magnetic resonance imaging (MRI) in predicting treatment outcome for OCD patients on the group- and individual-level. METHODS: In this retrospective study, we analyzed preoperative MRI data of a large cohort of patients who received DBS for OCD (n = 57). We used voxel-based morphometry to investigate whether grey matter (GM) or white matter (WM) volume surrounding the DBS electrode (nucleus accumbens (NAc), anterior thalamic radiation), and whole-brain GM/WM volume were associated with OCD severity and response status at 12-month follow-up. In addition, we performed machine learning analyses to predict treatment outcome at an individual-level and evaluated its performance using cross-validation. RESULTS: Larger preoperative left NAc volume was associated with lower OCD severity at 12-month follow-up (pFWE < 0.05). None of the individual-level regression/classification analyses exceeded chance-level performance. CONCLUSIONS: These results provide evidence that patients with larger NAc volumes show a better response to DBS, indicating that DBS success is partly determined by individual differences in brain anatomy. However, the results also indicate that structural MRI data alone does not provide sufficient information to guide clinical decision making at an individual level yet.

Trauma-focused psychotherapy response in youth with posttraumatic stress disorder is associated with changes in insula volume.

Randomized controlled trials have shown efficacy of trauma-focused psychotherapies in youth with posttraumatic stress disorder (PTSD), but little is known about the relationship between treatment response and alternations in brain structures associated with PTSD. In this study, we longitudinally examined the association between treatment response and pre-to posttreatment changes in structural magnetic resonance imaging (MRI) scans using a voxel-based morphometry approach. We analyzed MRI scans of 35 patients (ages 8-18 years, 21 female) with PTSD (80%) or partial PTSD (20%) before and after eight weekly sessions of trauma-focused psychotherapy. PTSD severity was assessed longitudinally using the Clinician-Administered PTSD scale for Children and Adolescents to divide participants into responders and non-responders. Group by time interaction analysis showed significant differences in grey-matter volume in the bilateral insula due to volume reductions over time in non-responders compared to responders. Despite the significant group by time interaction, there were no significant group differences at baseline or follow-up. As typical development is associated with insula volume increase, these longitudinal MRI findings suggest that treatment non-response is associated with atypical neurodevelopment of the insula, which may underlie persistence of PTSD in youth. The absence of structural MRI changes in treatment responders, while in need of replication, suggest that successful trauma-focused psychotherapy may not directly normalize brain abnormalities associated with PTSD.

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.

Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks.

Resting-state functional magnetic resonance imaging (rs-fMRI) data are 4-dimensional volumes (3-space + 1-time) that have been posited to reflect the underlying mechanisms of information exchange between brain regions, thus making it an attractive modality to develop diagnostic biomarkers of brain dysfunction. The enormous success of deep learning in computer vision has sparked recent interest in applying deep learning in neuroimaging. But the dimensionality of rs-fMRI data is too high (~20 M), making it difficult to meaningfully process the data in its raw form for deep learning experiments. It is currently not clear how the data should be engineered to optimally extract the time information, and whether combining different representations of time could provide better results. In this paper, we explored various transformations that retain the full spatial resolution by summarizing the temporal dimension of the rs-fMRI data, therefore making it possible to train a full three-dimensional convolutional neural network (3D-CNN) even on a moderately sized [~2,000 from Autism Brain Imaging Data Exchange (ABIDE)-I and II] data set. These transformations summarize the activity in each voxel of the rs-fMRI or that of the voxel and its neighbors to a single number. For each brain volume, we calculated regional homogeneity, the amplitude of low-frequency fluctuations, the fractional amplitude of low-frequency fluctuations, degree centrality, eigenvector centrality, local functional connectivity density, entropy, voxel-mirrored homotopic connectivity, and auto-correlation lag. We trained the 3D-CNN on a publically available autism dataset to classify the rs-fMRI images as being from individuals with autism spectrum disorder (ASD) or from healthy controls (CON) at an individual level. We attained results competitive on this task for a combined ABIDE-I and II datasets of ~66%. When all summary measures were combined the result was still only as good as that of the best single measure which was regional homogeneity (ReHo). In addition, we also applied the support vector machine (SVM) algorithm on the same dataset and achieved comparable results, suggesting that 3D-CNNs could not learn additional information from these temporal transformations that were more useful to differentiate ASD from CON.

Individual prediction of psychotherapy outcome in posttraumatic stress disorder using neuroimaging data.

Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. We investigated whether structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) data could distinguish between treatment responders and non-responders on the group and individual level. Forty-four male veterans with PTSD underwent baseline scanning followed by trauma-focused psychotherapy. Voxel-wise gray matter volumes were extracted from the structural MRI data and resting-state networks (RSNs) were calculated from rs-fMRI data using independent component analysis. Data were used to detect differences between responders and non-responders on the group level using permutation testing, and the single-subject level using Gaussian process classification with cross-validation. A RSN centered on the bilateral superior frontal gyrus differed between responders and non-responder groups (PFWE < 0.05) while a RSN centered on the pre-supplementary motor area distinguished between responders and non-responders on an individual-level with 81.4% accuracy (P < 0.001, 84.8% sensitivity, 78% specificity and AUC of 0.93). No significant single-subject classification or group differences were observed for gray matter volume. This proof-of-concept study demonstrates the feasibility of using rs-fMRI to develop neuroimaging biomarkers for treatment response, which could enable personalized treatment of patients with PTSD.

Individual Prediction of Behavioral Variant Frontotemporal Dementia Development Using Multivariate Pattern Analysis of Magnetic Resonance Imaging Data.

BACKGROUND: Patients with behavioral variant of frontotemporal dementia (bvFTD) initially may only show behavioral and/or cognitive symptoms that overlap with other neurological and psychiatric disorders. The diagnostic accuracy is dependent on progressive symptoms worsening and frontotemporal abnormalities on neuroimaging findings. Predictive biomarkers could facilitate the early detection of bvFTD. OBJECTIVE: To determine the prognostic accuracy of clinical and structural MRI data using a support vector machine (SVM) classification to predict the 2-year clinical follow-up diagnosis in a group of patients presenting late-onset behavioral changes. METHODS: Data from 73 patients were included and divided into probable/definite bvFTD (n = 18), neurological (n = 28), and psychiatric (n = 27) groups based on 2-year follow-up diagnosis. Grey-matter volumes were extracted from baseline structural MRI scans. SVM classifiers were used to perform three binary classifications: bvFTD versus neurological and psychiatric, bvFTD versus neurological, and bvFTD versus psychiatric group(s), and one multi-class classification. Classification performance was determined for clinical and neuroimaging data separately and their combination using 5-fold cross-validation. RESULTS: Accuracy of the binary classification tasks ranged from 72-82% (p < 0.001) with adequate sensitivity (67-79%), specificity (77-88%), and area-under-the-receiver-operator-curve (0.80-0.9). Multi-class accuracy ranged between 55-59% (p < 0.001). The combination of clinical and voxel-wise whole brain data showed the best performance overall. CONCLUSION: These results show the potential for automated early confirmation of diagnosis for bvFTD using machine learning analysis of clinical and neuroimaging data in a diverse and clinically relevant sample of patients.

Recreational use of GHB is associated with alterations of resting state functional connectivity of the central executive and default mode networks.

Gamma-hydroxybutyrate acid (GHB) is a recreational drug with a high addictive potential. Severe side effects such as GHB-induced coma are common and linked to increased emergency room attendances. Task-based functional-imaging studies have revealed an association between the regular use of GHB and multiple GHB-induced comas, and altered neurocognitive function. However the effects of multiple GHB-induced comas and regular GHB-use on intrinsic brain connectivity during rest remain unknown. The study population consisted of 23 GHB-users with ≥4 GHB-induced comas (GHB-Coma), 22 GHB-users who never experienced a GHB-induced coma (GHB-NoComa) and 24 polydrug users who never used GHB (No-GHB). Resting-state scans were collected to assess resting-state functional-connectivity within and between the default mode network (DMN), the bilateral central executive network (CEN) and the salience network (SN). The GHB-NoComa group showed decreased rsFC of the right CEN with a region in the anterior cingulate cortex (pFWE = 0.048) and decreased rsFC between the right CEN and the DMN (pFWE = 0.048) when compared with the No-GHB group. These results suggest that regular GHB-use is associated with decreased rsFC within the right CEN and between the right CEN and the DMN. The presence of multiple GHB-induced comas is not associated with (additional) alterations in rsFC.

Turning wounds into wisdom: Posttraumatic growth over the course of two types of trauma-focused psychotherapy in patients with PTSD.

BACKGROUND: Treatment studies in PTSD patients have mostly focused on adverse psychopathological outcomes whereas positive outcomes have received less attention. Objectives of this study were to investigate posttraumatic growth in response to two different psychotherapies, to examine the relationship between symptom improvement and growth, and to determine if posttraumatic growth predicted treatment response. METHODS: Outpatients diagnosed with PTSD after various types of trauma (n = 116) participated in a randomized controlled trial that compared Brief Eclectic Psychotherapy for PTSD (BEP) and Eye Movement Desensitization and Reprocessing therapy (EMDR). Posttraumatic growth was assessed pre- and post-treatment. PTSD severity was measured weekly. RESULTS: Posttraumatic growth scores significantly increased after trauma-focused psychotherapy, as well as scores in the subdomains personal strength, new possibilities, relating to others, and appreciation of life. Greater self-reported and clinician-rated PTSD decline was significantly related to greater increase in posttraumatic growth. No changes were found between treatment conditions, except for a stronger correlation between PTSD symptom decrease and increase in relating to others in BEP as compared to EMDR. No predictive effects were found. LIMITATIONS: We were unable to control for time effects because for ethical reasons, no control group not receiving treatment was included, and the stability of the changes could not be determined. CONCLUSIONS: Findings indicate that increases in posttraumatic growth accompany symptom decline in EMDR and BEP, and that these changes occur independent of whether the treatment specifically addresses posttraumatic growth as therapeutic process. Further research is encouraged to disentangle the contribution of therapeutic elements to growth.

Default Network Activity Is Associated with Better Performance in a Vigilance Task.

When attention has to be maintained over prolonged periods performance slowly fluctuates and errors can occur. It has been shown that lapses of attention are correlated with BOLD signals in frontal and parietal cortex. This raises the question how attentional fluctuations are linked to the fronto-parietal default network. Because the attentional state fluctuates slowly we expect that potential links between attentional fluctuations and brain activity should be observable on longer time scales and importantly also before the execution of the task. In the present study we used fMRI to identify brain activity that is correlated with vigilance, defined as fluctuations of reaction times (RT) during a sustained attention task. We found that brain activity in visual cortex, parietal lobe (PL), inferior and superior frontal gyrus, and supplementary motor area (SMA) was higher when the subject had a relatively long RT. In contrast to our expectations, activity in the default network (DN) was higher when subjects had a relatively short RT, that means when the performance was improved. This modulation in the DN was present already several seconds before the task execution, thus pointing to activity in the DN as a potential cause of performance increases in simple repetitive tasks.