Discovering networks altered by potential threat ("anxiety") using quadratic discriminant analysis

Threat of shock increases distractor susceptibility during the short-term maintenance of visual information

Elevated arousal in anxiety is thought to affect attention control. To test this, we designed a visual short-term memory (VSTM) task to examine distractor suppression during periods of threat and no-threat. We hypothesized that threat would impair performance when subjects had to filter out large numbers of distractors. The VSTM task required subjects to attend to one array of squares while ignoring a separate array. The number of target and distractor squares varied systematically, with high (four squares) and low (two squares) target and distractor conditions. This study comprised two separate experiments. Experiment 1 used startle responses and white noise as to directly measure threat-induced anxiety. Experiment 2 used BOLD to measure brain responses. For Experiment 1, subjects showed significantly larger startle responses during threat compared to safe period, supporting the validity of the threat manipulation. For Experiment 2, we found that accuracy was affected by threat, such that the distractor load negatively impacted accuracy only in the threat condition. We also found threat-related differences in parietal cortex activity. Overall, these findings suggest that threat affects distractor susceptibility, impairing filtering of distracting information. This effect is possibly mediated by hyperarousal of parietal cortex during threat.

Independence Threat or Interdependence Threat? The Focusing Effect on Social or Physical Threat Modulates Brain Activity

OBJECTIVE

The neural basis of threat perception has mostly been examined separately for social or physical threats. However, most of the threats encountered in everyday life are complex. The features of interactions between social and physiological threats under different attentional conditions are unclear.

METHOD

The present study explores this issue using an attention-guided paradigm based on ERP techniques. The screen displays social threats (face threats) and physical threats (action threats), instructing participants to concentrate on only one type of threat, thereby exploring brain activation characteristics.

RESULTS

It was found that action threats did not affect the processing of face threats in the face-attention condition, and electrophysiological evidence from the brain suggests a comparable situation to that when processing face threats alone, with higher amplitudes of the N170 and EPN (Early Posterior Negativity) components of anger than neutral emotions. However, when focusing on the action-attention condition, the brain was affected by face threats, as evidenced by a greater N190 elicited by stimuli containing threatening emotions, regardless of whether the action was threatening or not. This trend was also reflected in EPN.

CONCLUSIONS

The current study reveals important similarities and differences between physical and social threats, suggesting that the brain has a greater processing advantage for social threats.

Chemogenetic activation of corticotropin-releasing factor-expressing neurons in the anterior bed nucleus of the stria terminalis reduces effortful motivation behaviors

Corticotropin-releasing factor (CRF) in the anterior bed nucleus of the stria terminalis (aBNST) is associated with chronic stress and avoidance behavior. However, CRF + BNST neurons project to reward- and motivation-related brain regions, suggesting a potential role in motivated behavior. We used chemogenetics to selectively activate CRF+ aBNST neurons in male and female CRF-ires-Cre mice during an effort-related choice task and a concurrent choice task. In both tasks, mice were given the option either to exert effort for high value rewards or to choose freely available low value rewards. Acute chemogenetic activation of CRF+ aBNST neurons reduced barrier climbing for a high value reward in the effort-related choice task in both males and females. Furthermore, acute chemogenetic activation of CRF+ aBNST neurons also reduced effortful lever pressing in high-performing males in the concurrent choice task. These data suggest a novel role for CRF+ aBNST neurons in effort-based decision and motivation behaviors.

A neural oscillatory signature of sustained anxiety

BACKGROUND

Anxiety is a sustained response to uncertain threats; yet few studies have explored sustained neurobiological activities underlying anxious states, particularly spontaneous neural oscillations. To address this gap, we reanalysed magnetoencephalographic (MEG) data recorded during induced anxiety to identify differences in sustained oscillatory activity between high- and low-anxiety states.

METHODS

We combined data from three previous MEG studies in which healthy adults (total N = 51) were exposed to alternating periods of threat of unpredictable shock and safety while performing a range of cognitive tasks (passive oddball, mixed-saccade or stop-signal tasks). Spontaneous, band-limited, oscillatory activity was extracted from middle and late intervals of the threat and safe periods, and regional power distributions were reconstructed with adaptive beamforming. Conjunction analyses were used to identify regions showing overlapping spectral power differences between threat and safe periods across the three task paradigms.

RESULTS

MEG source analyses revealed a robust and widespread reduction in beta (14-30 Hz) power during threat periods in bilateral sensorimotor cortices extending into right prefrontal regions. Alpha (8-13 Hz) power reductions during threat were more circumscribed, with notable peaks in left intraparietal sulcus and thalamus.

CONCLUSIONS

Threat-induced anxiety is underpinned by a sustained reduction in spontaneous beta- and alpha-band activity in sensorimotor and parietal cortical regions. This general oscillatory pattern likely reflects a state of heightened action readiness and vigilance to cope with uncertain threats. Our findings provide a critical reference for which to identify abnormalities in cortical oscillatory activities in clinically anxious patients as well as evaluating the efficacy of anxiolytic treatments.

Threat of shock increases distractor susceptibility during the short-term maintenance of visual information

BACKGROUND

Work on anxiety related attention control deficits suggests that elevated arousal impacts the ability to filter out distractors. To test this, we designed a task to look at distractor suppression during periods of threat. We administered trials of a visual short-term memory (VSTM) task, during periods of unpredictable threat, and hypothesized that threat would impair performance during trials where subjects were required to filter out large numbers of distractors.

METHOD

Experiment 1 involved fifteen healthy participants who completed one study visit. They performed four runs of a VSTM task comprising 32 trials each. Participants were presented with an arrow indicating left or right, followed by an array of squares. They were instructed to remember the target side and disregard the distractors on the off-target side. A subsequent target square was shown, and participants indicated whether it matched one of the previously presented target squares. The trial conditions included 50% matches and 50% mismatches, with an equal distribution of left and right targets. The number of target and distractor squares varied systematically, with high (4 squares) and low (2 squares) target and distractor conditions. Trials alternated between periods of safety and threat, with startle responses recorded using electromyography (EMG) following white noise presentations. Experiment 2 involved twenty-seven healthy participants who completed the same VSTM task inside an MRI scanner during a single study visit. The procedure mirrored that of Experiment 1, except for the absence of white noise presentations.

RESULTS

For Experiment 1, subjects showed significantly larger startle responses during threat compared to safe period, supporting the validity of the threat manipulation. However, results suggested that the white noise probes interfered with performance. For Experiment 2, we found that both accuracy was affected by threat, such that distractor load negatively impacted accuracy only in the threat condition.

CONCLUSION

Overall, these findings suggest that threat affects distractor susceptibility during the short-term maintenance of visual information. The presence of threat makes it more difficult to filter out distracting information. We believe that this is related to hyperarousal of parietal cortex, which has been observed during unpredictable threat.

Chronic stress-induced synaptic changes to corticotropin-releasing factor-signaling in the bed nucleus of the stria terminalis

The sexually dimorphic bed nucleus of the stria terminalis (BNST) is comprised of several distinct regions, some of which act as a hub for stress-induced changes in neural circuitry and behavior. In rodents, the anterodorsal BNST is especially affected by chronic exposure to stress, which results in alterations to the corticotropin-releasing factor (CRF)-signaling pathway, including CRF receptors and upstream regulators. Stress increases cellular excitability in BNST CRF+ neurons by potentiating miniature excitatory postsynaptic current (mEPSC) amplitude, altering the resting membrane potential, and diminishing M-currents (a voltage-gated K+ current that stabilizes membrane potential). Rodent anterodorsal and anterolateral BNST neurons are also critical regulators of behavior, including avoidance of aversive contexts and fear learning (especially that of sustained threats). These rodent behaviors are historically associated with anxiety. Furthermore, BNST is implicated in stress-related mood disorders, including anxiety and Post-Traumatic Stress Disorders in humans, and may be linked to sex differences found in mood disorders.

Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers

Alcohol use disorder (AUD) is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use to assess the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity (rsFNC) derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve (AUC) of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity.

Modeling anxiety in healthy humans: a key intermediate bridge between basic and clinical sciences

Animal models of anxiety disorders are important for elucidating neurobiological defense mechanisms. However, animal models are limited when it comes to understanding the more complex processes of anxiety that are unique to humans (e.g., worry) and to screen new treatments. In this review, we outline how the Experimental Psychopathology approach, based on experimental models of anxiety in healthy subjects, can mitigate these limitations and complement research in animals. Experimental psychopathology can bridge basic research in animals and clinical studies, as well as guide and constrain hypotheses about the nature of psychopathology, treatment mechanisms, and treatment targets. This review begins with a brief review of the strengths and limitations of animal models before discussing the need for human models of anxiety, which are especially necessary to probe higher-order cognitive processes. This can be accomplished by combining anxiety-induction procedures with tasks that probe clinically relevant processes to identify neurocircuits that are potentially altered by anxiety. The review then discusses the validity of experimental psychopathology and introduces a methodological approach consisting of five steps: (1) select anxiety-relevant cognitive or behavioral operations and associated tasks, (2) identify the underlying neurocircuits supporting these operations in healthy controls, 3) examine the impact of experimental anxiety on the targeted operations in healthy controls, (4) utilize findings from step 3 to generate hypotheses about neurocircuit dysfunction in anxious patients, and 5) evaluate treatment mechanisms and screen novel treatments. This is followed by two concrete illustrations of this approach and suggestions for future studies.

Acute stress alters the 'default' brain processing

Active adaptation to acute stress is essential for coping with daily life challenges. The stress hormone cortisol, as well as large scale re-allocations of brain resources have been implicated in this adaptation. Stress-induced shifts between large-scale brain networks, including salience (SN), central executive (CEN) and default mode networks (DMN), have however been demonstrated mainly under task-conditions. It remains unclear whether such network shifts also occur in the absence of ongoing task-demands, and most critically, whether these network shifts are predictive of individual variation in the magnitude of cortisol stress-responses. In a sample of 335 healthy participants, we investigated stress-induced functional connectivity changes (delta-FC) of the SN, CEN and DMN, using resting-state fMRI data acquired before and after a socially evaluated cold-pressor test and a mental arithmetic task. To investigate which network changes are associated with acute stress, we evaluated the association between cortisol increase and delta-FC of each network. Stress-induced cortisol increase was associated with increased connectivity within the SN, but with decreased coupling of DMN at both local (within network) and global (synchronization with brain regions also outside the network) levels. These findings indicate that acute stress prompts immediate connectivity changes in large-scale resting-state networks, including the SN and DMN in the absence of explicit ongoing task-demands. Most interestingly, this brain reorganization is coupled with individuals' cortisol stress-responsiveness. These results suggest that the observed stress-induced network reorganization might function as a neural mechanism determining individual stress reactivity and, therefore, it could serve as a promising marker for future studies on stress resilience and vulnerability.

MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis

The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets.

Objective Assessment of Patient Inhaler User Technique Using an Audio-Based Classification Approach

Many patients make critical user technique errors when using pressurised metered dose inhalers (pMDIs) which reduce the clinical efficacy of respiratory medication. Such critical errors include poor actuation coordination (poor timing of medication release during inhalation) and inhaling too fast (peak inspiratory flow rate over 90 L/min). Here, we present a novel audio-based method that objectively assesses patient pMDI user technique. The Inhaler Compliance Assessment device was employed to record inhaler audio signals from 62 respiratory patients as they used a pMDI with an In-Check Flo-Tone device attached to the inhaler mouthpiece. Using a quadratic discriminant analysis approach, the audio-based method generated a total frame-by-frame accuracy of 88.2% in classifying sound events (actuation, inhalation and exhalation). The audio-based method estimated the peak inspiratory flow rate and volume of inhalations with an accuracy of 88.2% and 83.94% respectively. It was detected that 89% of patients made at least one critical user technique error even after tuition from an expert clinical reviewer. This method provides a more clinically accurate assessment of patient inhaler user technique than standard checklist methods.

Threat of shock increases excitability and connectivity of the intraparietal sulcus

Anxiety disorders affect approximately 1 in 5 (18%) Americans within a given 1 year period, placing a substantial burden on the national health care system. Therefore, there is a critical need to understand the neural mechanisms mediating anxiety symptoms. We used unbiased, multimodal, data-driven, whole-brain measures of neural activity (magnetoencephalography) and connectivity (fMRI) to identify the regions of the brain that contribute most prominently to sustained anxiety. We report that a single brain region, the intraparietal sulcus (IPS), shows both elevated neural activity and global brain connectivity during threat. The IPS plays a key role in attention orienting and may contribute to the hypervigilance that is a common symptom of pathological anxiety. Hyperactivation of this region during elevated state anxiety may account for the paradoxical facilitation of performance on tasks that require an external focus of attention, and impairment of performance on tasks that require an internal focus of attention.

DOI:http://dx.doi.org/10.7554/eLife.23608.001

Anxiety disorders affect around one in five Americans, and in many cases people experience anxiety so intensely that they have difficulties performing day-to-day activities. To help these people, it is important to understand how anxiety works. Current research suggests that anxiety disorders are caused when the connections in the brain that control our response to threat are either excessively or inappropriately activated. However, it was not clear what causes the anxiety to last for long periods. To better understand this phenomenon, Balderston et al. studied the brains of over 30 volunteers using two types of measurements called magnetoencephalography and fMRI.

In the each experiment, participants experienced periods of threat, where they could receive unpredictable electric shocks. In the first experiment, Balderston et al. measured the brain activity by recording the magnetic fields generated in the brain. In the second experiment, they used fMRI to record changes in the blood flow throughout the brain to measure how the different regions in the brain communicate.

The recordings identified a single part of the brain that increased its activity and changed its communication pattern with the other regions in the brain, when people are anxious. This region in a part of the brain called parietal lobe, is also important for processing attention, which suggests that anxiety might make people also more aware of their surroundings. However, this extra awareness might also make it more difficult for people to concentrate.

Future studies may be able to stimulate this area of the brain through the scalp to potentially reduce anxiety, as the affected area is close to the skull.

DOI:http://dx.doi.org/10.7554/eLife.23608.002

Strategies for Advancing Disease Definition Using Biomarkers and Genetics: The Bipolar and Schizophrenia Network for Intermediate Phenotypes

It is critical for psychiatry as a field to develop approaches to define the molecular, cellular, and circuit basis of its brain diseases, especially for serious mental illnesses, and then to use these definitions to generate biologically based disease categories, as well as to explore disease mechanisms and illness etiologies. Our current reliance on phenomenology is inadequate to support exploration of molecular treatment targets and disease formulations, and the leap directly from phenomenology to disease biology has been limiting because of broad heterogeneity within conventional diagnoses. The questions addressed in this review are formulated around how we can use brain biomarkers to achieve disease categories that are biologically based. We have grouped together a series of vignettes as examples of early approaches, all using the Bipolar and Schizophrenia Network on Intermediate Phenotypes (BSNIP) biomarker database and collaborators, starting off with describing the foundational statistical methods for these goals. We use primarily criterion-free statistics to identify pertinent groups of involved genes related to psychosis as well as symptoms, and finally, to create new biologically based disease cohorts within the psychopathological dimension of psychosis. Although we do not put these results forward as final formulations, they represent a novel effort to rely minimally on phenomenology as a diagnostic tool and to fully embrace brain characteristics of structure, as well as molecular and cellular characteristics and function, to support disease definition in psychosis.