Evaluating speech latencies during structured psychiatric interviews as an automated objective measure of psychomotor slowing.

We sought to derive an objective measure of psychomotor slowing from speech analytics during a psychiatric interview to avoid potential burden of dedicated neurophysiological testing. Speech latency, which reflects response time between speakers, shows promise from the literature. Speech data was obtained from 274 subjects with a diagnosis of bipolar I depression enrolled in a randomized, doubleblind, 6-week phase 2 clinical trial. Audio recordings of structured Montgomery-Åsberg Depression Rating Scale (MADRS) interviews at 6 time points were examined (k = 1,352). We evaluated speech latencies, and other aspects of speech, for temporal stability, convergent validity, sensitivity/responsivity to clinical change, and generalization across seven socio-linguistically diverse countries. Speech latency was minimally associated with demographic features, and explained nearly a third of the variance in depression (categorically defined). Speech latency significantly decreased as depression symptoms improved over time, explaining nearly 20 % of variance in depression remission. Classification for differentiating people with versus without concurrent depression was high (AUCs > 0.85) both cross-sectionally and longitudinally. Results replicated across countries. Other speech features offered modest incremental contribution. Neurophysiological speech parameters with face validity can be derived from psychiatric interviews without the added patient burden of additional testing.

Enrichment using speech latencies improves treatment effect size in a clinical trial of bipolar depression.

Clinical trials in depression lack objective measures. Speech latencies are an objective measure of psychomotor slowing with face validity and empirical support. 'Turn latency' is the response time between speakers. Retrospective analysis was carried-out on the utility of turn latencies as an enrichment tool in a clinical trial of bipolar I depression. Speech data was obtained from 274 participants during 1,352 Montgomery-Åsberg Depression Rating Scale (MADRS) recordings in a randomized, placebo controlled, 6-week clinical trial of SEP-4199 (200 mg or 400 mg). Post-randomization turn latencies were compared between patients with moderate to severe depression and patients whose depression had remitted. A cutoff was determined and applied to turn latencies pre-randomization to classify individuals into two groups: Speech Latencies Slow (SL-Slow) and Speech Latencies Normal (SL-Normal). At week 6, SL-Slow (N = 172) showed significant separation in MADRS scores between placebo and treatment arms. SL-Normal (N = 102) showed larger MADRS improvements and no significant separation between placebo and treatment arms. Excluding SL-Normal increased primary outcome effect size by 52 % and 100 % for the treatment arms. Turn latencies are an objective measure available from standard clinical assessments and may assess the severity of symptoms more accurately and screen out placebo responders.

Psilocybin desynchronizes the human brain.

A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.

Estimating heterogeneity of treatment effect in psychiatric clinical trials.

Currently, placebo-controlled clinical trials report mean change and effect sizes, which masks information about heterogeneity of treatment effects (HTE). Here, we present a method to estimate HTE and evaluate the null hypothesis (H0) that a drug has equal benefit for all participants (HTE=0). We developed measure termed 'estimated heterogeneity of treatment effect' or eHTE, which estimates variability in drug response by comparing distributions between study arms. This approach was tested across numerous large placebo-controlled clinical trials. In contrast with variance-based methods which have not identified heterogeneity in psychiatric trials, reproducible instances of treatment heterogeneity were found. For example, heterogeneous response was found in a trial of venlafaxine for depression (peHTE=0.034), and two trials of dasotraline for binge eating disorder (Phase 2, peHTE=0.002; Phase 3, 4mg peHTE=0.011; Phase 3, 6mg peHTE=0.003). Significant response heterogeneity was detected in other datasets as well, often despite no difference in variance between placebo and drug arms. The implications of eHTE as a clinical trial outcomes independent from central tendency of the group is considered and the important of the eHTE method and results for drug developers, providers, and patients is discussed.

Review and critique of current testing protocols for upper-limb prostheses: a call for standardization amidst rapid technological advancements.

This article provides a comprehensive narrative review of physical task-based assessments used to evaluate the multi-grasp dexterity and functional impact of varying control systems in pediatric and adult upper-limb prostheses. Our search returned 1,442 research articles from online databases, of which 25 tests-selected for their scientific rigor, evaluation metrics, and psychometric properties-met our review criteria. We observed that despite significant advancements in the mechatronics of upper-limb prostheses, these 25 assessments are the only validated evaluation methods that have emerged since the first measure in 1948. This not only underscores the lack of a consistently updated, standardized assessment protocol for new innovations, but also reveals an unsettling trend: as technology outpaces standardized evaluation measures, developers will often support their novel devices through custom, study-specific tests. These boutique assessments can potentially introduce bias and jeopardize validity. Furthermore, our analysis revealed that current validated evaluation methods often overlook the influence of competing interests on test success. Clinical settings and research laboratories differ in their time constraints, access to specialized equipment, and testing objectives, all of which significantly influence assessment selection and consistent use. Therefore, we propose a dual testing approach to address the varied demands of these distinct environments. Additionally, we found that almost all existing task-based assessments lack an integrated mechanism for collecting patient feedback, which we assert is essential for a holistic evaluation of upper-limb prostheses. Our review underscores the pressing need for a standardized evaluation protocol capable of objectively assessing the rapidly advancing prosthetic technologies across all testing domains.

Psilocybin desynchronizes brain networks.

1The relationship between the acute effects of psychedelics and their persisting neurobiological and psychological effects is poorly understood. Here, we tracked brain changes with longitudinal precision functional mapping in healthy adults before, during, and for up to 3 weeks after oral psilocybin and methylphenidate (17 MRI visits per participant) and again 6+ months later. Psilocybin disrupted connectivity across cortical networks and subcortical structures, producing more than 3-fold greater acute changes in functional networks than methylphenidate. These changes were driven by desynchronization of brain activity across spatial scales (area, network, whole brain). Psilocybin-driven desynchronization was observed across association cortex but strongest in the default mode network (DMN), which is connected to the anterior hippocampus and thought to create our sense of self. Performing a perceptual task reduced psilocybin-induced network changes, suggesting a neurobiological basis for grounding, connecting with physical reality during psychedelic therapy. The acute brain effects of psilocybin are consistent with distortions of space-time and the self. Psilocybin induced persistent decrease in functional connectivity between the anterior hippocampus and cortex (and DMN in particular), lasting for weeks but normalizing after 6 months. Persistent suppression of hippocampal-DMN connectivity represents a candidate neuroanatomical and mechanistic correlate for psilocybin's pro-plasticity and anti-depressant effects.

Better Biomarkers, Faster Drugs, Stronger Models: Progress Towards Precision Psychiatry.

The 21st century has brought novel therapies and new therapeutic targets for major depressive disorder (MDD). Until recently all antidepressant medications targeted monoamines-serotonin, norepinephrine, and dopamine- and their regulatory systems. But growing evidence has suggested that individuals who fail to respond to a monoaminergic treatment are likely to fail to respond to other monoaminergic options. The emergence in recent years of treatment targets beyond the monoaminergic systems (e.g. κ-opioid antagonists, ketamine and other NMDA modulators, neurosteroids) has cultivated hopes for not only greater efficacy in treating depression, but also improved precision in targeting specific phenotypes and symptoms. Concurrently, an expanding repertoire of diagnostic and assessment tools-such as smartphone-based experience sampling and brain imaging-is moving the field toward more reliable and symptom-specific measurement with greater descriptive and prescriptive power. Taken together, these diagnostic tools and treatment options herald a new era of "precision psychiatry"-the selection and implementation of an optimal treatment for an individual patient's particular needs. Anhedonia offers an example of the new precision psychiatry. Anhedonia has moved from merely one among several criteria for depression to a transdiagnostic psychopathology which can be understood neurobiologically, assessed quantitatively, and centered as a primary target in research and development of novel pharmacotherapies. We describe functional testing of reward circuits in the development of kappa-opioid antagonists for anhedonia. This offers a lens for understanding how and under what circumstances other novel treatments, such as psychedelics, might find a place in the future landscape of precision psychiatric care.

Psychedelic Drug Legislative Reform and Legalization in the US.

Importance: Psychedelic drugs are becoming accessible in the US through a patchwork of state legislative reforms. This shift necessitates consensus on treatment models, education and guidance for health care professionals, and planning for implementation and regulation. Objective: To assess trends in psychedelics legislative reform and legalization in the US to provide guidance to health care professionals, policy makers, and the public. Evidence Review: Data were compiled from legislative databases (BillTrack50, LexisNexis, and Ballotpedia) from January 1, 2019, to September 28, 2022. Legislation was identified by searching for terms related to psychedelics (eg, psilocybin, MDMA, peyote, mescaline, ibogaine, LSD, ayahuasca, and DMT). Bills were coded by an attorney along 2 axes: which psychedelic drugs would be affected and in what ways (eg, decriminalization, funding for medical research, and right to try). To explore drivers and rates of legislative reform, data were compared with other state indices including 2020 presidential voting margins and marijuana legislative reform. Findings: Twenty-five states have considered 74 bills (69 legislative initiatives, 5 ballot measures); 10 bills were enacted, and 32 were still active. The number of psychedelic reform bills introduced during each calendar year increased steadily from 5 in 2019 to 6 in 2020, 27 in 2021, and 36 in 2022. Nearly all bills specified psilocybin (67 [90%]), and many also included MDMA (3,4-methylenedioxy-methamphetamine; 27 [36%]). While bills varied in their framework, most (43 [58%]) proposed decriminalization, of which few delineated medical oversight (10 of 43 [23%]) or training and/or licensure requirements (15 of 43 [35%]). In general, bills contained less regulatory guidance than the enacted Oregon Measure 109. While early legislative efforts occurred in liberal states, the margin between liberal and conservative states has decreased over time (although the difference was not significant), suggesting that psychedelic drug reform is becoming a bipartisan issue. In addition, an analytic model based on marijuana legalization projected that a majority of states will legalize psychedelics by 2034 to 2037. Conclusions and Relevance: Legislative reform for psychedelic drugs has been proceeding in a rapid, patchwork fashion in the US. Further consideration should be given to key health care issues such as establishing (1) standards for drugs procured outside the medical establishment, (2) licensure criteria for prescribers and therapists, (3) clinical and billing infrastructure, (4) potential contraindications, and (5) use in special populations like youths, older adults, and pregnant individuals.

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization.

In a world dependent on road-based transportation, it is essential to understand automobiles. We propose an acoustic road vehicle characterization system as an integrated approach for using sound captured by mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We develop and implement novel deep learning cascading architectures, which we define as conditional, multi-level networks that process raw audio to extract highly granular insights for vehicle understanding. To showcase the viability of cascading architectures, we build a multi-task convolutional neural network that predicts and cascades vehicle attributes to enhance misfire fault detection. We train and test these models on a synthesized dataset reflecting more than 40 hours of augmented audio. Through cascading fuel type, engine configuration, cylinder count and aspiration type attributes, our cascading CNN achieves 87.0% test set accuracy on misfire fault detection which demonstrates margins of 8.0% and 1.7% over naïve and parallel CNN baselines. We explore experimental studies focused on acoustic features, data augmentation, and data reliability. Finally, we conclude with a discussion of broader implications, future directions, and application areas for this work.

Mapping correlated neurological deficits after stroke to distributed brain networks.

Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.

Temporal exponential random graph models of longitudinal brain networks after stroke.

Plasticity after stroke is a complex phenomenon. Functional reorganization occurs not only in the perilesional tissue but throughout the brain. However, the local connection mechanisms generating such global network changes remain largely unknown. To address this question, time must be considered as a formal variable of the problem rather than a simple repeated observation. Here, we hypothesized that the presence of temporal connection motifs, such as the formation of temporal triangles (T) and edges (E) over time, would explain large-scale brain reorganization after stroke. To test our hypothesis, we adopted a statistical framework based on temporal exponential random graph models (tERGMs), where the aforementioned temporal motifs were implemented as parameters and adapted to capture global network changes after stroke. We first validated the performance on synthetic time-varying networks as compared to standard static approaches. Then, using real functional brain networks, we showed that estimates of tERGM parameters were sufficient to reproduce brain network changes from 2 weeks to 1 year after stroke. These temporal connection signatures, reflecting within-hemisphere segregation (T) and between hemisphere integration (E), were associated with patients' future behaviour. In particular, interhemispheric temporal edges significantly correlated with the chronic language and visual outcome in subcortical and cortical stroke, respectively. Our results indicate the importance of time-varying connection properties when modelling dynamic complex systems and provide fresh insights into modelling of brain network mechanisms after stroke.

Unified waste metrics: A gamified tool in next-generation strategic planning.

A gamification approach for tackling waste management planning and urban development provide a more engaging and interactive experience with high pedagogical potential. Existing serious games involving waste management are complex in their data ingestion, use, and presentation, limiting individuals' opportunities to gain knowledge and decision-making skills transferrable to the real world. Simulations, by comparison, provide either an oversimplified and unrealistic user interface or explore in depth individual rather than aggregate key performance indicators for waste management, limiting potential knowledge retention. There is a clear opportunity in creating an informative, easy-to-use simulation-based game to help stakeholders build understanding of waste management policies, performance, and causal relationships. This gamified tool provides clear feedback through quick-visibility performance indicators (i.e., waste accumulation index, waste compositional analysis, prevention activities etc.) and offers the opportunity, through multi-criteria decision making, of simulating real-life scenarios and previewing the possible outcomes of certain in-game actions. The research question is how the process of gamification might serve as powerful tool for educating decision makers. The results are considered as a reference point to any policy maker intending to assess environmental performance, proposed activities to reach Circular Economy targets, and European Green Deal and UN Sustainable Development Goals.

Psilocybin-assisted psychotherapy for depression: Emerging research on a psychedelic compound with a rich history.

There is a serious need for novel therapies that treat individuals with depression, including major depressive disorder (MDD) and treatment-resistant depression (TRD). An emerging body of research has demonstrated that psychedelic drugs such as psilocybin, combined with supportive psychotherapy, exert rapid and sustained antidepressant effects. The use of psychedelics is not new: they have a rich history with evidence of their use in ritual and medical settings. However, due to political, social, and cultural pressures, their use was limited until modern clinical trials began to emerge in the 2010s. This review provides a comprehensive look at the potential use of psilocybin in the treatment of depression and TRD. It includes an overview of the history, pharmacology, and proposed mechanism of psilocybin, and describes several published studies in the last decade which have provided evidence of the efficacy and safety of psilocybin-assisted psychotherapy for individuals with depression. It also includes a discussion of the limitations and barriers of current research on psychedelics. The results of these studies are contextualized within the current treatment landscape through an overview of the pathophysiology of depression and the treatments currently in use, as well as the clinical needs these novel therapies have the promise to fulfill.

Individualized Functional Subnetworks Connect Human Striatum and Frontal Cortex.

The striatum and cerebral cortex are interconnected via multiple recurrent loops that play a major role in many neuropsychiatric conditions. Primate corticostriatal connections can be precisely mapped using invasive tract-tracing. However, noninvasive human research has not mapped these connections with anatomical precision, limited in part by the practice of averaging neuroimaging data across individuals. Here we utilized highly sampled resting-state functional connectivity MRI for individual-specific precision functional mapping (PFM) of corticostriatal connections. We identified ten individual-specific subnetworks linking cortex-predominately frontal cortex-to striatum, most of which converged with nonhuman primate tract-tracing work. These included separable connections between nucleus accumbens core/shell and orbitofrontal/medial frontal gyrus; between anterior striatum and dorsomedial prefrontal cortex; between dorsal caudate and lateral prefrontal cortex; and between middle/posterior putamen and supplementary motor/primary motor cortex. Two subnetworks that did not converge with nonhuman primates were connected to cortical regions associated with human language function. Thus, precision subnetworks identify detailed, individual-specific, neurobiologically plausible corticostriatal connectivity that includes human-specific language networks.

Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke.

Recent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity between task-negative and task-positive resting-state networks. Whole-brain computational modelling studies, at the individual subject level, using undirected effective connectivity derived from empirically measured functional connectivity, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional effective connectivity from zero-lagged and lagged covariance matrices, then, to compare it to empirically measured functional connectivity for predicting stroke versus healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of functional connectivity versus model effective connectivity in predicting the long-term outcome from acute measures. Both functional and effective connectivity predicted healthy from stroke individuals significantly better than the chance-level; however, accuracy for the effective connectivity was significantly higher than for functional connectivity at 1- to 2-week, 3-month and 1-year post-stroke. Predictive functional connections mainly included those reported in previous studies (within-network inter-hemispheric and between task-positive and -negative networks intra-hemispherically). Predictive effective connections included additional between-network links. Effective connectivity was a better predictor than functional connectivity of the number of behavioural domains in which patients suffered deficits, both at 2-week and 1-year post-onset of stroke. Interestingly, patient deficits at 1-year time-point were better predicted by effective connectivity values at 2 weeks rather than at 1-year time-point. Our results thus demonstrate that the second-order statistics of functional MRI resting-state activity at an early stage of stroke, derived from a whole-brain effective connectivity, estimated in a model fitted to reproduce the propagation of neuronal activity, has pertinent information for clinical prognosis.

Prolonged ketamine infusion modulates limbic connectivity and induces sustained remission of treatment-resistant depression.

Ketamine produces a rapid antidepressant response in over 50% of adults with treatment-resistant depression. A long infusion of ketamine may provide durable remission of depressive symptoms, but the safety, efficacy, and neurobiological correlates are unknown. In this open-label, proof-of-principle study, adults with treatment-resistant depression (N = 23) underwent a 96-h infusion of intravenous ketamine (0.15 mg/kg/h titrated toward 0.6 mg/kg/h). Clonidine was co-administered to reduce psychotomimetic effects. We measured clinical response for 8 weeks post-infusion. Resting-state functional magnetic resonance imaging was used to assess functional connectivity in patients pre- and 2 weeks post-infusion and in matched non-depressed controls (N = 27). We hypothesized that responders to therapy would demonstrate response-dependent connectivity changes while all subjects would show treatment-dependent connectivity changes. Most participants completed infusion (21/23; mean final dose 0.54 mg/kg/h, SD 0.13). The infusion was well tolerated with minimal cognitive and psychotomimetic side effects. Depressive symptoms were markedly reduced (MADRS 29 ± 4 at baseline to 9 ± 8 one day post-infusion), which was sustained at 2 weeks (13 ± 8) and 8 weeks (15 ± 8). Imaging demonstrated a response-dependent decrease in hyperconnectivity of the subgenual anterior cingulate cortex to the default mode network, and a treatment-dependent decrease in hyperconnectivity within the limbic system (hippocampus, amygdala, medial thalamus, nucleus accumbens). In exploratory analyses, connectivity was increased between the limbic system and frontal areas, and smaller right hippocampus volume at baseline predicted larger MADRS change. A single prolonged infusion of ketamine provides a tolerated, rapid, and sustained response in treatment-resistant depression and normalizes depression-related hyperconnectivity in the limbic system and frontal lobe. ClinicalTrials.gov : Treatment Resistant Depression (Pilot), NCT01179009.

Integrative and Network-Specific Connectivity of the Basal Ganglia and Thalamus Defined in Individuals.

The basal ganglia, thalamus, and cerebral cortex form an interconnected network implicated in many neurological and psychiatric illnesses. A better understanding of cortico-subcortical circuits in individuals will aid in development of personalized treatments. Using precision functional mapping-individual-specific analysis of highly sampled human participants-we investigated individual-specific functional connectivity between subcortical structures and cortical functional networks. This approach revealed distinct subcortical zones of network specificity and multi-network integration. Integration zones were systematic, with convergence of cingulo-opercular control and somatomotor networks in the ventral intermediate thalamus (motor integration zones), dorsal attention and visual networks in the pulvinar, and default mode and multiple control networks in the caudate nucleus. The motor integration zones were present in every individual and correspond to consistently successful sites of deep brain stimulation (DBS; essential tremor). Individually variable subcortical zones correspond to DBS sites with less consistent treatment effects, highlighting the importance of PFM for neurosurgery, neurology, and psychiatry.