Examining the short-term anxiolytic and antidepressant effect of Floatation-REST

<div><p>Floatation-REST (Reduced Environmental Stimulation Therapy) reduces sensory input to the nervous system through the act of floating supine in a pool of water saturated with Epsom salt. The float experience is calibrated so that sensory signals from visual, auditory, olfactory, gustatory, thermal, tactile, vestibular, gravitational and proprioceptive channels are minimized, as is most movement and speech. This open-label study aimed to examine whether Floatation-REST would attenuate symptoms of anxiety, stress, and depression in a clinical sample. Fifty participants were recruited across a spectrum of anxiety and stress-related disorders (posttraumatic stress, generalized anxiety, panic, agoraphobia, and social anxiety), most (n = 46) with comorbid unipolar depression. Measures of self-reported affect were collected immediately before and after a 1-hour float session, with the primary outcome measure being the pre- to post-float change score on the Spielberger State Anxiety Inventory. Irrespective of diagnosis, Floatation-REST substantially reduced state anxiety (estimated Cohen’s d > 2). Moreover, participants reported significant reductions in stress, muscle tension, pain, depression and negative affect, accompanied by a significant improvement in mood characterized by increases in serenity, relaxation, happiness and overall well-being (p < .0001 for all variables). In reference to a group of 30 non-anxious participants, the effects were found to be more robust in the anxious sample and approaching non-anxious levels during the post-float period. Further analysis revealed that the most severely anxious participants reported the largest effects. Overall, the procedure was well-tolerated, with no major safety concerns stemming from this single session. The findings from this initial study need to be replicated in larger controlled trials, but suggest that Floatation-REST may be a promising technique for transiently reducing the suffering in those with anxiety and depression.</p><p><b>Trial registration:</b> ClinicalTrials.gov <a href="https://clinicaltrials.gov/show/NCT03051074" target="_blank">NCT03051074</a></p></div

Flow diagram for study recruitment.

<p>Flow diagram for study recruitment.</p

Estimated effect size of a single float session in patients with anxiety and depression.

<p>The estimated Cohen’s d is shown for each pre- to post-float change score, with grey lines representing the 95% confidence interval. The dashed black line demarcates the starting point (d = 0.8) for what is considered a “large effect size” [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190292#pone.0190292.ref073" target="_blank">73</a>].</p

Floatation-REST in an open circular float pool.

<p>The circular fiberglass pool is 8 feet in diameter and contains 11 inches of reverse osmosis water saturated with ~1,800 pounds of USP grade Epsom salt (magnesium sulfate), creating a dense salt water solution that is maintained at a specific gravity of ~1.26, allowing participants to effortlessly float on their back while the water hovers just above the ears. A small blue LED light remains illuminated throughout the float session, and can be turned off by the participant through a round air switch (both of which can be seen in the picture, located immediately adjacent to the participant’s right foot). Unlike the picture, clothing is usually not worn while floating since anything touching the body can generate somatosensory stimulation, potentially detracting from the float experience.</p

Participant demographics and baseline functioning on the day of Floatation-REST.

<p>Participant demographics and baseline functioning on the day of Floatation-REST.</p

Side effect checklist.

<p>After the float session, participants completed a 43-item side effect checklist. For each item participants selected one of four choices (None, Mild, Moderate, or Extreme) and each choice was automatically scored as a number (0, 1, 2, or 3). Shown here is the average score across the group of 50 anxious and depressed participants, with error bars representing the standard error of the mean (SEM).</p

Impact of Floatation-REST on mood and affect.

<p>Change scores from pre- to post-float are shown for all 16 measures. To facilitate comparisons across measures the score for each measure was converted to POMP units representing the percent of maximum possible on each scale. All measures showed a significant pre- to post-float change with the significance level denoted with asterisks. Error bars represent the SEM.</p

Impact of Floatation-REST on state anxiety.

<p>(A) The float experience caused a reduction in state anxiety that was evident across all 50 participants, leading to a significant pre- to post-float change on the Spielberger State Anxiety Inventory (STAI) at the group level [t(49) = -15.16, p < .0001, d = 2.15]. (B) Despite a large baseline difference, the anxious group’s average post-float anxiety had reached levels slightly lower than the pre-float anxiety reported by the non-anxious reference sample. Error bars represent the SEM.</p

Inclusion and exclusion criteria.

<p>Inclusion and exclusion criteria.</p

Data_Sheet_1_Predicting Age From Brain EEG Signals—A Machine Learning Approach.DOCX

<p>Objective: The brain age gap estimate (BrainAGE) is the difference between the estimated age and the individual chronological age. BrainAGE was studied primarily using MRI techniques. EEG signals in combination with machine learning (ML) approaches were not commonly used for the human age prediction, and BrainAGE. We investigated whether age-related changes are affecting brain EEG signals, and whether we can predict the chronological age and obtain BrainAGE estimates using a rigorous ML framework with a novel and extensive EEG features extraction.</p><p>Methods: EEG data were obtained from 468 healthy, mood/anxiety, eating and substance use disorder participants (297 females) from the Tulsa-1000, a naturalistic longitudinal study based on Research Domain Criteria framework. Five sets of preprocessed EEG features across channels and frequency bands were used with different ML methods to predict age. Using a nested-cross-validation (NCV) approach and stack-ensemble learning from EEG features, the predicted age was estimated. The important features and their spatial distributions were deduced.</p><p>Results: The stack-ensemble age prediction model achieved R² = 0.37 (0.06), Mean Absolute Error (MAE) = 6.87(0.69) and RMSE = 8.46(0.59) in years. The age and predicted age correlation was r = 0.6. The feature importance revealed that age predictors are spread out across different feature types. The NCV approach produced a reliable age estimation, with features consistent behavior across different folds.</p><p>Conclusion: Our rigorous ML framework and extensive EEG signal features allow a reliable estimation of chronological age, and BrainAGE. This general framework can be extended to test EEG association with and to predict/study other physiological relevant responses.</p