Parsing the neural correlates of anxious apprehension and anxious arousal in the grey-matter of healthy youth

Structural Brain Correlates of Anxiety During Development

Anxiety and related disorders are prevalent across the lifespan, often have their onset during youth, and are associated with notable levels of impairment and burden across multiple domains. Elucidating the associations between differential patterns of neurodevelopment and anxiety in youth is a promising approach for developing deeper insights regarding the neurobiological etiologies and maintenance factors associated with anxiety and related disorders. A growing body of literature has yielded evidence of associations between patterns of brain structure (i.e., volume, cortical thickness, and cortical surface area) and anxiety. Here, we present a review and synthesis of the existing body of literature surrounding neurostructural correlates of anxiety in youth spanning multiple anxiety presentations and three neurostructural modalities. We reveal substantially more research focusing on brain volume than cortical thickness or surface area and a greater number of studies examining anxiety broadly defined, obsessive-compulsive disorder, or posttraumatic stress disorder. There is also evidence of considerable variability in the brain regions implicated and the direction of associations across studies. Finally, we discuss the gaps and limitations in this body of work, which suggest avenues for future directions.

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

Brain morphometry points to emerging patterns of psychosis, depression, and anxiety vulnerability over a 2-year period in childhood

BACKGROUND

Gray matter morphometry studies have lent seminal insights into the etiology of mental illness. Existing research has primarily focused on adults and then, typically on a single disorder. Examining brain characteristics in late childhood, when the brain is preparing to undergo significant adolescent reorganization and various forms of serious psychopathology are just first emerging, may allow for a unique and highly important perspective of overlapping and unique pathogenesis.

METHODS

A total of 8645 youth were recruited as part of the Adolescent Brain and Cognitive Development study. Magnetic resonance imaging scans were collected, and psychotic-like experiences (PLEs), depressive, and anxiety symptoms were assessed three times over a 2-year period. Cortical thickness, surface area, and subcortical volume were used to predict baseline symptomatology and symptom progression over time.

RESULTS

Some features could possibly signal common vulnerability, predicting progression across forms of psychopathology (e.g. superior frontal and middle temporal regions). However, there was a specific predictive value for emerging PLEs (lateral occipital and precentral thickness), anxiety (parietal thickness/area and cingulate), and depression (e.g. parahippocampal and inferior temporal).

CONCLUSION

Findings indicate common and distinct patterns of vulnerability for varying forms of psychopathology are present during late childhood, before the adolescent reorganization, and have direct relevance for informing novel conceptual models along with early prevention and intervention efforts.

Gray-Matter Morphometry of Internalizing-Symptom Dimensions During Adolescence

Understanding the neuroanatomical correlates of internalizing psychopathology during adolescence may shed light on to neurodevelopmental processes that make this a critical period for the trajectory of mental illness. However, few studies have simultaneously examined co-occurring and dissociable features of internalizing psychopathology during this formative developmental stage. In the current study we identify the neuroanatomical correlates of four dimensions of internalizing psychopathology symptoms in adolescents: a common internalizing dimension capturing covariance in symptoms across internalizing disorders, as well as low positive affect-, anxious arousal-, and anxious apprehension-specific residuals. Our results suggest that these dimensions are associated with neuroanatomy across much of the brain, including prefrontal and limbic regions implicated in case-control studies, but also regions supporting visual processing. Importantly, results differed between males and females in regions that are sexually dimorphic in adulthood and the direction of the effects were largely opposite to what has been observed in adults and children.

Predictive utility of symptom measures in classifying anxiety and depression: A machine-learning approach

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent, co-occurring disorders with significant symptom overlap, posing challenges in accurately distinguishing and diagnosing these disorders. The tripartite model proposes that anxious arousal is specific to anxiety and anhedonia is specific to depression, though anxious apprehension may play a greater role in GAD than anxious arousal. The present study tested the efficacy of the Mood and Anxiety Symptom Questionnaire anhedonic depression (MASQ-AD) and anxious arousal (MASQ-AA) scales and the Penn State Worry Questionnaire (PSWQ) in identifying lifetime or current MDD, current major depressive episode (MDE), and GAD using binary support vector machine learning algorithms in an adult sample (n = 150). The PSWQ and MASQ-AD demonstrated predictive utility in screening for and identification of GAD and current MDE respectively, with the MASQ-AD eight-item subscale outperforming the MASQ-AD 14-item subscale. The MASQ-AA did not predict MDD, current MDE, or GAD, and the MASQ-AD did not predict current or lifetime MDD. The PSWQ and MASQ-AD are efficient and accurate screening tools for GAD and current MDE. Results support the tripartite model in that anhedonia is unique to depression, but inclusion of anxious apprehension as a separate dimension of anxiety is warranted.

Subclinical depression and anxiety impact verbal memory functioning differently in men and women -an fMRI study

BACKGROUND

Depressive symptoms are known to affect memory efficiency in various populations. More specifically, several studies conducted in patients suffering from schizophrenia have indicated that memory efficiency is affected by depressed mood in female patients and by anxiety in male patients. We investigated, using neuroimaging techniques, whether similar gender-specific associations with subclinical depression and anxiety could be observed in a non-clinical sample.

METHOD

Forty-five healthy Spanish-speaking individuals (23 females) were administered a verbal memory task. Lists of high- and low-frequency words were presented. Immediate free recall was requested after the learning of each list, and a yes/no recognition task was completed during the acquisition of the fMRI data.

RESULTS

Regression analyses revealed that higher depression scores in women, and higher anxiety scores in men, were associated with poorer recall. In women, higher depression scores were further associated with decreased cerebral activity in the right temporoparietal junction, left inferior occipitotemporal gyrus, bilateral thalamus, and left anterior cingulate during correct recognition of target words. In men, anxiety scores were not associated with any cerebral activity.

CONCLUSIONS

Subclinical depression in women appears to affect memory efficiency by impacting cerebral regions specifically recruited for the cognitive demands of the task, as well as cerebral regions more generally involved in arousal, decision-making, and emotional regulation. Anxiety in men might impact the encoding memory processes. The results, although preliminary, suggest that gender differences may need to be taken into account when developing strategies for the cognitive and pharmacological remediation of memory impairment.

Common and specific dimensions of internalizing disorders are characterized by unique patterns of brain activity on a task of emotional cognitive control

Alterations in neural systems underlying cognitive control are well-documented across individuals with various internalizing disorders. The current study examined how individual differences in underlying traits related to internalizing disorders influence brain activation, as assessed by fMRI, when cognitive control must be exerted to make a decision about the emotional valence (positive, negative) of a task-relevant word displayed concurrently with a task-irrelevant emotional face. Taking a bi-factor model approach, fifty-five middle-aged female participants were characterized on symptom level on a common internalizing latent factor representing shared symptoms across anxiety and depression, as well as on specific factors remaining after taking the common internalizing factor into account: low positive affect, anxious arousal, and anxious apprehension. Contrasting activation on trials requiring higher vs. lower control revealed that higher levels of the Common Internalizing factor are associated with less deactivation of regions of the default mode network. Higher levels of the Low Positive Affect-specific factor are associated with less differentiation in engagement of portions of the fronto-parietal control network, while higher levels of the Anxious Arousal-specific factor are associated with less of a differentiation in activation of the thalamus. No effects were observed for level of the Anxious Apprehension-specific factor. These results suggest that prior findings of alterations in default mode activity associated with depression may not be specific to depressive symptoms per se but may characterize internalizing symptoms more generally. In addition, they suggest that reduced engagement of cognitive control regions may be more associated with low positive affect than depressive symptoms more generally.

Amygdala-prefrontal cortex white matter tracts are widespread, variable and implicated in amygdala modulation in adolescents

The amygdala is critically involved in processing emotion. Through bidirectional connections, the prefrontal cortex (PFC) is hypothesized to influence amygdala reactivity. However, research that elucidates the nature of amygdala-PFC interactions - through mapping amygdala-prefrontal tracts, quantifying variability among tracts, and linking this variability to amygdala activation - is lacking. Using probabilistic tractography to map amygdala-prefrontal white matter connectivity in 142 adolescents, the present study found that white matter connectivity was greater between the amygdala and the subgenual cingulate, orbitofrontal (OFC), and dorsomedial (dmPFC) prefrontal regions than with the dorsal cingulate and dorsolateral regions. Then, using a machine-learning regression, we found that greater amygdala-PFC white matter connectivity was related to attenuated amygdala reactivity. This effect was driven by amygdala white matter connectivity with the dmPFC and OFC, supporting implicit emotion processing theories which highlight the critical role of these regions in amygdala regulation. This study is among the first to map and compare specific amygdala-prefrontal white matter tracts and to relate variability in connectivity to amygdala activation, particularly among a large sample of adolescents from a well-sampled study. By examining the association between specific amygdala-PFC tracts and amygdala activation, the present study provides novel insight into the nature of this emotion-based circuit.

Individual differences in trait anxiety are associated with gray matter alterations in hypothalamus: Preliminary neuroanatomical evidence

Trait anxiety is particularly a prone phenotype for the development of anxiety disorders and depression. Studying the neural underpinnings of trait anxiety can further inform our understanding of the etiology of these disorders. To investigate the structural correlates of trait anxiety, high resolution structural images were acquired from 76 right-handed healthy participants and gray matter volumes were extracted from a priori regions of interest (ROIs) that were earlier implicated in anxiety like behaviour (i.e., hippocampus, amygdala, anterior cingulate cortex, thalamus, hypothalamus and prefrontal (dorsolateral, rostrolateral, ventrolateral) cortex. In a partial correlation analysis (with age, gender, and Beck Depression Inventory (BDI) scores as covariates of no interest), trait anxiety was found to be negatively correlated with the gray matter volume of hypothalamus bilaterally and positively correlated with the gray matter volume of left thalamus. In a hierarchical multiple regression analysis, grey matter volume of hypothalamus and left thalamus were found to be the significant predictors of trait anxiety. Our findings thus suggest that a smaller gray matter volume in the hypothalamus and an increase in the gray matter volume of left thalamus is related to a disposition to high anxiety personality trait.