Frontostriatal network dysfunction as a domain-general mechanism underlying phantom perception.

In the present study, we use resting state fMRI to investigate whether nucleus accumbens (NAc) and extended frontostriatal networks are involved in the pathology of auditory phantom perception, i.e., tinnitus, through a study of functional connectivity. We hypothesize that resting state functional connectivity involving NAc will be increased relative to what is observed in healthy subjects and that this connectivity will correlate with clinical measures of tinnitus such as percept loudness, duration of symptoms, etc. We show that a large sample of patients with chronic tinnitus (n = 90) features extensive functional connectivity involving NAc that is largely absent in healthy subjects (n = 94). We further show that connectivity involving NAc correlates significantly with tinnitus percept loudness and the duration of tinnitus symptoms, even after controlling for the effects of age and hearing loss. The loudness correlation, which involves NAc and parahippocampal cortex, is consistent with existing literature identifying the parahippocampus as a tinnitus generator. Our results further suggest that frontostriatal connectivity may predict the transition from acute to chronic tinnitus, analogous to what is seen in the pain literature. We discuss these ideas and suggest fruitful avenues for future research.

Automatic detection of pain using machine learning.

Pain is one of the most common symptoms reported by individuals presenting to hospitals and clinics and is associated with significant disability and economic impacts; however, the ability to quantify and monitor pain is modest and typically accomplished through subjective self-report. Since pain is associated with stereotypical physiological alterations, there is potential for non-invasive, objective pain measurements through biosensors coupled with machine learning algorithms. In the current study, a physiological dataset associated with acute pain induction in healthy adults was leveraged to develop an algorithm capable of detecting pain in real-time and in natural field environments. Forty-one human subjects were exposed to acute pain through the cold pressor test while being monitored using electrocardiography. A series of respiratory and heart rate variability features in the time, frequency, and nonlinear domains were calculated and used to develop logistic regression classifiers of pain for two scenarios: (1) laboratory/clinical use with an F1 score of 81.9% and (2) field/ambulatory use with an F1 score of 79.4%. The resulting pain algorithms could be leveraged to quantify acute pain using data from a range of sources, such as ECG data in clinical settings or pulse plethysmography data in a growing number of consumer wearables. Given the high prevalence of pain worldwide and the lack of objective methods to quantify it, this approach has the potential to identify and better mitigate individual pain.

Automated stress detection using mobile application and wearable sensors improves symptoms of mental health disorders in military personnel.

Leading causes in global health-related burden include stress, depression, anger, fatigue, insomnia, substance abuse, and increased suicidality. While all individuals are at risk, certain career fields such as military service are at an elevated risk. Cognitive behavioral therapy (CBT) is highly effective at treating mental health disorders but suffers from low compliance and high dropout rates in military environments. The current study conducted a randomized controlled trial with military personnel to assess outcomes for an asymptomatic group (n = 10) not receiving mental health treatment, a symptomatic group (n = 10) using a mHealth application capable of monitoring physiological stress via a commercial wearable alerting users to the presence of stress, guiding them through stress reduction techniques, and communicating information to providers, and a symptomatic control group (n = 10) of military personnel undergoing CBT. Fifty percent of symptomatic controls dropped out of CBT early and the group maintained baseline symptoms. In contrast, those who used the mHealth application completed therapy and showed a significant reduction in symptoms of depression, anxiety, stress, and anger. The results from this study demonstrate the feasibility of pairing data-driven mobile applications with CBT in vulnerable populations, leading to an improvement in therapy compliance and a reduction in symptoms compared to CBT treatment alone. Future work is focused on the inclusion of passive sensing modalities and the integration of additional data sources to provide better insights and inform clinical decisions to improve personalized support.

Prediction and perception: Insights for (and from) tinnitus.

More than 150 years have passed since Helmholtz first described perception as a process of unconscious inference about the causes of sensations. His ideas have since inspired a wealth of literature investigating the mechanisms underlying these inferences. In recent years, much of this work has converged on the notion that the brain is a hierarchical generative model of its environment that predicts sensations and updates itself based on prediction errors. Here, we build a case for modeling tinnitus from this perspective, i.e. predictive coding. We emphasize two key claims: (1) acute tinnitus reflects an increase in sensory precision in related frequency channels and (2) chronic tinnitus reflects a change in the brain's default prediction. We further discuss specific neural biomarkers that would constitute evidence for or against these claims. Finally, we explore the implications of our model for clinical intervention strategies. We conclude that predictive coding offers the basis for a unifying theory of cognitive neuroscience, which we demonstrate with several examples linking tinnitus to other lines of brain research.

Meta-analysis of functional subdivisions within human posteromedial cortex.

The posteromedial cortex (PMC)-comprising posterior cingulate cortex (PCC), retrosplenial cortex (RSC), and the precuneus (PrC)-is perhaps best known for its involvement in the default mode network. There is no consensus regarding the specific functions of PMC, however, and its component regions each exhibit distinct, but partially overlapping functional profiles. To date, there has been minimal effort to disentangle the functions of these regions. In the present study, we use Neurosynth ( http://neurosynth.org ) to conduct an unbiased meta-analysis of the PMC based on fMRI coactivation and semantic information extracted from 11,406 studies. Our analyses revealed six PMC clusters with distinct functional profiles: superior and inferior dorsal PCC, anterior and posterior PrC, ventral PCC, and RSC. We discuss these findings in the context of the existing literature and suggest several fruitful avenues for continued research.

Functional brain changes in auditory phantom perception evoked by different stimulus frequencies.

Bayesian models of brain function such as active inference and predictive coding offer a general theoretical framework with which to explain several aspects of normal and disordered brain function. Of particular interest to the present study is the potential for such models to explain the pathology of auditory phantom perception, i.e. tinnitus. To test this framework empirically, we perform an fMRI experiment on a large clinical sample (n = 75) of the human chronic tinnitus population. The experiment features a within-subject design based on two experimental conditions: subjects were presented with sound stimuli matched to their tinnitus frequency (TF) as well as similar stimuli presented at a control frequency (CF). The responses elicited by these stimuli, as measured using both activity and functional connectivity, were then analyzed both within and between conditions. Given the Bayesian-brain framework, we hypothesize that TF stimuli will elicit greater activity and/or functional connectivity in areas related to the cognitive and emotional aspects of tinnitus, i.e. tinnitus-related distress. We conversely hypothesize that CF stimuli will elicit greater activity/connectivity in areas related to auditory perception and attention. We discuss our results in the context of this framework and suggest future directions for empirical testing.

Functional connectivity analysis of fMRI data collected from human subjects with chronic tinnitus and varying levels of tinnitus-related distress.

The data presented here are functional connectivity analyses based on fMRI scans from a clinical sample of the chronic tinnitus population (n = 75). All data were obtained during an experiment in which subjects listened to auditory stimuli via headphones while undergoing fMRI scanning. The stimuli consisted of tones and bandpass noise presented at different frequencies. Stimulus frequency was the experimental factor, which was set (1) at each subject's tinnitus percept frequency (TF) and (2) at an unrelated control frequency (CF) at least one octave away from the TF stimuli. All subjects were presented with stimuli at these two frequencies. We refer the reader to our original research article "Functional brain changes in auditory phantom perception evoked by different stimulus frequencies" (Hullfish et al., 2018) for further discussion. Here, we present data specifically from group-level analyses where the subjects were divided according to their level of tinnitus-related distress. The high-distress (HD) group comprised 43 subjects with Tinnitus Questionnaire (TQ) scores greater than or equal to 47, out of a possible 82 points. The low-distress (LD) group comprised the remaining 32 subjects with TQ score less than 47. The data presented include contrasts of functional connectivity elicited by TF and CF stimuli in each group as well as contrasts between the two groups.