Motor neuron disease (MND) is a progressive, fatal, neurodegenerative condition that affects motor neurons in the brain and spinal cord, resulting in loss of the ability to move, speak, swallow and breathe. Acceptance and commitment therapy (ACT) is an acceptance-based behavioural therapy that may be particularly beneficial for people living with MND (plwMND). This qualitative study aimed to explore plwMND’s experiences of receiving adapted ACT, tailored to their specific needs, and therapists’ experiences of delivering it.

Semi-structured qualitative interviews were conducted with plwMND who had received up to eight 1:1 sessions of adapted ACT and therapists who had delivered it within an uncontrolled feasibility study. Interviews explored experiences of ACT and how it could be optimised for plwMND. Interviews were audio recorded, transcribed and analysed using framework analysis.

Participants were 14 plwMND and 11 therapists. Data were coded into four over-arching themes: (i) an appropriate tool to navigate the disease course; (ii) the value of therapy outweighing the challenges; (iii) relevance to the individual; and (iv) involving others. These themes highlighted that ACT was perceived to be acceptable by plwMND and therapists, and many participants reported or anticipated beneficial outcomes in the future, despite some therapeutic challenges. They also highlighted how individual factors can influence experiences of ACT, and the potential benefit of involving others in therapy.

Qualitative data supported the acceptability of ACT for plwMND. Future research and clinical practice should address expectations and personal relevance of ACT to optimise its delivery to plwMND.

1. (1) To understand the views of people living with motor neuron disease (plwMND) and therapists on acceptance and commitment therapy (ACT) for people living with this condition.

2. (2) To understand the facilitators of and barriers to ACT for plwMND.

3. (3) To learn whether ACT that has been tailored to meet the specific needs of plwMND needs to be further adapted to potentially increase its acceptability to this population.

Therapeutics targeting frontotemporal dementia (FTD) are entering clinical trials. There are challenges to conducting these studies, including the relative rarity of the disease. Remote assessment tools could increase access to clinical research and pave the way for decentralized clinical trials. We developed the ALLFTD Mobile App, a smartphone application that includes assessments of cognition, speech/language, and motor functioning. The objectives were to determine the feasibility and acceptability of collecting remote smartphone data in a multicenter FTD research study and evaluate the reliability and validity of the smartphone cognitive and motor measures.

A diagnostically mixed sample of 207 participants with FTD or from familial FTD kindreds (CDR®+NACC-FTLD=0 [n=91]; CDR®+NACC-FTLD=0.5 [n=39]; CDR®+NACC-FTLD>1 [n=39]; unknown [n=38]) were asked to remotely complete a battery of tests on their smartphones three times over two weeks. Measures included five executive functioning (EF) tests, an adaptive memory test, and participant experience surveys. A subset completed smartphone tests of balance at home (n=31) and a finger tapping test (FTT) in the clinic (n=11). We analyzed adherence (percentage of available measures that were completed) and user experience. We evaluated Spearman-Brown split-half reliability (100 iterations) using the first available assessment for each participant. We assessed test-retest reliability across all available assessments by estimating intraclass correlation coefficients (ICC). To investigate construct validity, we fit regression models testing the association of the smartphone measures with gold-standard neuropsychological outcomes (UDS3-EF composite [Staffaroni et al., 2021], CVLT3-Brief Form [CVLT3-BF] Immediate Recall, mechanical FTT), measures of disease severity (CDR®+NACC-FTLD Box Score & Progressive Supranuclear Palsy Rating Scale [PSPRS]), and regional gray matter volumes (cognitive tests only).

Participants completed 70% of tasks. Most reported that the instructions were understandable (93%), considered the time commitment acceptable (97%), and were willing to complete additional assessments (98%). Split-half reliability was excellent for the executive functioning (r’s=0.93-0.99) and good for the memory test (r=0.78). Test-retest reliabilities ranged from acceptable to excellent for cognitive tasks (ICC: 0.70-0.96) and were excellent for the balance (ICC=0.97) and good for FTT (ICC=0.89). Smartphone EF measures were strongly associated with the UDS3-EF composite (ß's=0.6-0.8, all p<.001), and the memory test was strongly correlated with total immediate recall on the CVLT3-BF (ß=0.7, p<.001). Smartphone FTT was associated with mechanical FTT (ß=0.9, p=.02), and greater acceleration on the balance test was associated with more motor features (ß=0.6, p=0.02). Worse performance on all cognitive tests was associated with greater disease severity (ß's=0.5-0.7, all p<.001). Poorer performance on the smartphone EF tasks was associated with smaller frontoparietal/subcortical volume (ß's=0.4-0.6, all p<.015) and worse memory scores with smaller hippocampal volume (ß=0.5, p<.001).

These results suggest remote digital data collection of cognitive and motor functioning in FTD research is feasible and acceptable. These findings also support the reliability and validity of unsupervised ALLFTD Mobile App cognitive tests and provide preliminary support for the motor measures, although further study in larger samples is required.

Despite emerging evidence suggesting the efficacy of psilocybin in the treatment of mood disorders such as depression, the exact mechanisms by which psilocybin is able to elicit these antidepressant effects remains unknown.

As the use of psilocybin as a treatment modality for depression has garnered increasing interest, this study aims to summarize the existing evidence of the mechanism of action with which psilocybin alleviates depressive symptoms, focusing specifically on the neurobiological effects of psilocybin in human subjects.

Four databases (Ovid MEDLINE, EMBASE, psychINFO, and Web of Science) were searched using a combination of MeSH terms and free text keywords in September 2021. The original search included both human and animal studies and must have included testing of the mechanism of action of psilocybin. Only antidepressant effects were considered, with no other mood disorders or psychiatric diagnoses included. Two independent researchers screened at every stage of the review, with a third researcher resolving any conflicts. Though a full systematic review outlining the current literature on the complete mechanisms of action of psilocybin on depression was conducted, this abstract will focus specifically on the nine papers that included human subjects, disregarding the five animal models. PROSPERO registration number: 282710.

After removing duplicates, the search identified 2193 papers and forty-nine were selected for full text review. Out of nine papers outlining the mechanisms of action of psilocybin use in human subjects, three papers investigated psilocybin’s effect on serotonin or glutamate receptor activity, two found an increase in synaptogenesis in regions such as the medial frontal cortex and hippocampus. Four found variation in blood flow to the amygdala, two found altered blood flow to the prefrontal cortex, and one found a reduction in delta power during sleep. Four papers found changes in functional connectivity or neurotransmission, most commonly in the hippocampus or prefrontal cortex.

Overall, the exact mechanism of psilocybin’s potential antidepressant effect remains unclear. Multiple pathways may be involved, including alterations in serotonin and glutamate receptor activity, as well as shifts in amygdala activity, neurogenesis, and functional connectivity in various brain regions. The relative lack of studies, and the variety of neurobiological modalities and endpoints used challenged the consolidation of data into consensus findings. Further studies are needed to better characterize psilocybin’s mechanism of action and to better understand the clinical effects of the use of psilocybin in the treatment of depression.

None Declared

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

The transfer rate for patients from an Alternate Care Site (ACS) back to a hospital may serve as a metric of appropriate patient selection and the ability of an ACS to treat moderate to severely ill patients accepted from overwhelmed health-care systems. During the coronavirus infectious disease 2019 (COVID-19) pandemic, hospitals worldwide experienced acute surges of patients presenting with acute respiratory failure.

An ACS in Imperial County, California was re-established in November 2020 to help decompress 2 local hospitals experiencing surges of COVID-19 cases. The patients treated often had multiple comorbid illnesses and required a median supplemental oxygen of 3 L/min (LPM) on admission. Numerous interventions were initiated during a 2-wk period to improve clinical care delivery.

The objectives of this retrospective observational study are to evaluate the impact of these clinical and staff interventions at an ACS on the transfer rate and to provide issues to consider for future ACS sites managing COVID-19 patients.

The data suggest that continuous, real-time process-improvement interventions helped reduce the transfer rate back to hospitals from 36.7% to 14.5% and that an ACS is a viable option for managing symptomatic COVID-19 positive patients requiring hospital-level care when hospitals are overburdened.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.