The utility of mouse and rat studies critically depends on their replicability in other laboratories. A widely advocated approach to improving replicability is through the rigorous control of predefined animal or experimental conditions, known as standardization. However, this approach limits the generalizability of the findings to only to the standardized conditions and is a potential cause rather than solution to what has been called a replicability crisis. Alternative strategies include estimating the heterogeneity of effects across laboratories, either through designs that vary testing conditions, or by direct statistical analysis of laboratory variation. We previously evaluated our statistical approach for estimating the interlaboratory replicability of a single laboratory discovery. Those results, however, were from a well-coordinated, multi-lab phenotyping study and did not extend to the more realistic setting in which laboratories are operating independently of each other. Here, we sought to test our statistical approach as a realistic prospective experiment, in mice, using 152 results from 5 independent published studies deposited in the Mouse Phenome Database (MPD). In independent replication experiments at 3 laboratories, we found that 53 of the results were replicable, so the other 99 were considered non-replicable. Of the 99 non-replicable results, 59 were statistically significant (at 0.05) in their original single-lab analysis, putting the probability that a single-lab statistical discovery was made even though it is non-replicable, at 59.6%. We then introduced the dimensionless "Genotype-by-Laboratory" (GxL) factor-the ratio between the standard deviations of the GxL interaction and the standard deviation within groups. Using the GxL factor reduced the number of single-lab statistical discoveries and alongside reduced the probability of a non-replicable result to be discovered in the single lab to 12.1%. Such reduction naturally leads to reduced power to make replicable discoveries, but this reduction was small (from 87% to 66%), indicating the small price paid for the large improvement in replicability. Tools and data needed for the above GxL adjustment are publicly available at the MPD and will become increasingly useful as the range of assays and testing conditions in this resource increases.

Systematic reviews and meta-analyses are important tools for synthesizing evidence from multiple studies. They serve to increase power and improve precision, in the same way that large studies can do, but also to establish the consistency of effects and replicability of results across studies. In this work we propose statistical tools to quantify replicability of effect signs (or directions) and their consistency. We suggest that these tools accompany the fixed-effect or random-effects meta-analysis, and we show that they convey important information for the assessment of the intervention under investigation. We motivate and demonstrate our approach and its implications by examples from systematic reviews from the Cochrane Library. Our tools make no assumptions on the distribution of the true effect sizes, so their inferential guarantees continue to hold even if the assumptions of the fixed-effect or random-effects models do not hold. We also develop a version of this tool under the fixed-effect assumption for cases where it is crucial and justified.

Introduction: Substance use disorders (SUDs) are a leading cause of morbidity and mortality worldwide, having a profound and global impact on health, well-being, safety, and productivity. Although traditionally the prevalence of SUDs in Israel has been estimated to be lower than those in high-income countries, estimates and characteristics of individuals with SUDs in the past decade are lacking. In this work, we explored the prevalence of SUDs among the adult Jewish population in Israel, per different classes of substances across sex, age group, and other sociodemographic factors. Methods: Data from an online representative sample of 4,025 respondents were collected, including the alcohol, smoking, and substance involvement screening test (ASSIST) metric and sociodemographic data. Results: We found that the most common SUDs were alcohol (10.5% [9.5–11.4]), cannabis (9.0% [8.2–9.9]), and sedative (3.6% [3.0–4.2]) use disorders. Alcohol-cannabis (3.2% [2.7–3.7]) and alcohol-sedative (1.04% [0.7–1.35]) were the most prevalent co-occurring SUDs. Among those with cannabis use disorder, the prevalence of alcohol use disorder was found to be 35.3% [30.4–40.2]. The estimated risk for alcohol use disorder was found to be inversely proportional to age, cannabis use disorder increased, peaked, and decreased with age, and that of sedative use disorder increased with age, particularly among women. While older individuals (in the 51–60 years of age group) were at lower risk (OR = 0.5 [0.3, 0.8]) compared to those <20 years of age for alcohol use disorder, they were at increased risk for sedative use disorder (OR = 3.1 [1.2, 9.7]). Conclusions: These findings represent substantially higher rates of SUDs in Israel than those previously reported and should affect resources allocated to addiction prevention and treatment. Further research on the role of gender, age, culture, and ethnicity in the propensity to develop SUDs is necessary for the development of more focused preventive and intervention measures. Focusing on non-Jewish populations in Israel and broadening the scope to include behavioral addictions should be addressed in future studies.

Experimentation with mouse and rat models has become a central strategy for discovering mammalian gene function, and for preclinical testing of pharmacological treatments, yet the utility of any findings critically depends on their replicability in other laboratories. In previous publications we proposed a statistical approach for estimating the inter-laboratory replicability of novel discoveries made in a single laboratory. We demonstrated that previous phenotyping results from multi-lab databases can be used to derive a Genotype-by-Lab (GxL) adjustment factor to greatly enhance the replicability of the single-lab findings, for similarly measured phenotypes, even before making the effort of replicating these finding in additional laboratories. This demonstration, however, still raised several important questions that could only be answered by an additional large-scale prospective experiment: 1) Does GxL-adjustment work in single-lab experiments that were not intended to be standardized across laboratories, and with genotypes that were not included in the previous experiments? And 2) Can it be used to adjust the results of pharmacological experiments? We investigated these questions by attempting to replicate, across three laboratories, results from five single-lab studies in the Mouse Phenome Database (MPD), offering 212 comparisons, including 60 involving a pharmacological treatment: 18 mg/kg/day fluoxetine. In addition, we define and use a dimensionless GxL factor, by dividing the GxL variance by the standard deviation between animals within groups, as a more robust vehicle to transfer the adjustment from the multi-lab analysis to very different labs and genotypes. For genotype comparisons, GxL-adjustment reduced the rate of non-replicable discoveries from 60% to 12%, for the price of reducing the power to make replicable discoveries from 87% to 66%. In absolute numbers, the adjustment prevented 23 non-replicable discoveries for the price of missing only three replicated ones. Tools and data needed for deployment of this method across other mouse experiments are publicly available in MPD. Our results further point at some phenotypes as more prone to produce non-replicable results, while others, known to be more difficult to measure, are as likely to produce replicable results (once adjusted) such as the physiological measure, body weight.

Background: Amygdala activity dysregulation plays a central role in post-traumatic stress disorder (PTSD). Hence learning to self-regulate one's amygdala activity may facilitate recovery. PTSD is further characterized by abnormal contextual processing related to the traumatic memory. Therefore, provoking the personal traumatic narrative while training amygdala down-regulation could enhance clinical efficacy. We report the results of a randomized controlled trial (NCT02544971) of a novel self-neuromodulation procedure (i.e. NeuroFeedback) for PTSD, aimed at down-regulating limbic activity while receiving feedback from an auditory script of a personal traumatic narrative. To scale-up applicability, neural activity was probed by an fMRI-informed EEG model of amygdala activity, termed Amygdala Electrical Finger-Print (AmygEFP). Methods: Fifty-nine adults meeting DSM-5 criteria for PTSD were randomized between three groups: Trauma script feedback interface (Trauma-NF) or Neutral feedback interface (Neutral-NF), and a control group of No NF (to control for spontaneous recovery). Before and immediately after 15 NF training sessions patients were blindly assessed for PTSD symptoms and underwent one session of amygdala fMRI-NF for transferability testing. Follow-up clinical assessment was performed at 3-and 6-months following NF treatment. Results: Patients in both NF groups learned to volitionally down-regulate AmygEFP signal and demonstrated a greater reduction in PTSD symptoms and improved down-regulation of the amygdala during fMRI-NF, compared to the No-NF group. The Trauma-NF group presented the largest immediate clinical improvement. Conclusions: This proof-of-concept study indicates the feasibility of the AmygEFP-NF process-driven as a scalable intervention for PTSD and illustrates its clinical potential. Further investigation is warranted to elucidate the contribution of AmygEFP-NF beyond exposure and placebo effects.

The activity-dependent neuroprotective protein (ADNP), a double-edged sword, sex-dependently regulates multiple genes and was previously associated with the control of early muscle development and aging. Here we aimed to decipher the involvement of ADNP in versatile muscle gene expression patterns in correlation with motor function throughout life. Using quantitative RT-PCR we showed that Adnp+/− heterozygous deficiency in mice resulted in aberrant gastrocnemius (GC) muscle, tongue and bladder gene expression, which was corrected by the Adnp snippet, drug candidate, NAP (CP201). A significant sexual dichotomy was discovered, coupled to muscle and age-specific gene regulation. As such, Adnp was shown to regulate myosin light chain (Myl) in the gastrocnemius (GC) muscle, the language acquisition gene forkhead box protein P2 (Foxp2) in the tongue and the pituitary-adenylate cyclase activating polypeptide (PACAP) receptor PAC1 mRNA (Adcyap1r1) in the bladder, with PACAP linked to bladder function. A tight age regulation was observed, coupled to an extensive correlation to muscle function (gait analysis), placing ADNP as a muscle-regulating gene/protein.

Activity-dependent neuroprotective protein (ADNP) mutations are linked with cognitive dysfunctions characterizing the autistic-like ADNP syndrome patients, who also suffer from delayed motor maturation. We thus hypothesized that ADNP is deregulated in versatile myopathies and that local ADNP muscle deficiency results in myopathy, treatable by the ADNP fragment NAP. Here, single-cell transcriptomics identified ADNP as a major constituent of the developing human muscle. ADNP transcript concentrations further predicted multiple human muscle diseases, with concentrations negatively correlated with the ADNP target interacting protein, microtubule end protein 1 (EB1). Reverting back to modeling at the single-cell level of the male mouse transcriptome, Adnp mRNA concentrations age-dependently correlated with motor disease as well as with sexual maturation gene transcripts, while Adnp expressing limb muscle cells significantly decreased with aging. Mouse Adnp heterozygous deficiency exhibited muscle microtubule reduction and myosin light chain (Myl2) deregulation coupled with motor dysfunction. CRISPR knockdown of adult gastrocnemius muscle Adnp in a Cas9 mouse resulted in treadmill (male) and gait (female) dysfunctions that were specifically ameliorated by treatment with the ADNP snippet, microtubule interacting, Myl2-regulating, NAP (CP201). Taken together, our studies provide new hope for personalized diagnosis/therapeutics in versatile myopathies.

Activity-dependent neuroprotective protein (ADNP) and its protein snippet NAP (drug candidate CP201) regulate synapse formation and cognitive as well as behavioral functions, in part, through microtubule interaction. Given potential interactions between the microbiome and brain function, we now investigated the potential effects of the ADNP-deficient genotype, mimicking the ADNP syndrome on microbiota composition in the Adnp +/– mouse model. We have discovered a surprising robust sexually dichotomized Adnp genotype effect and correction by NAP (CP201) as follows. Most of the commensal bacterial microbiota tested were affected by the Adnp genotype and corrected by NAP treatment in a male sex-dependent manner. The following list includes all the bacterial groups tested—labeled in bold are male Adnp —genotype increased and corrected (decreased) by NAP. (1) Eubacteriaceae (EubV3) , (2) Enterobacteriaceae (Entero), (3) Enterococcus genus (gEncocc), (4) Lactobacillus group (Lacto), (5) Bifidobacterium genus (BIF) , (6) Bacteroides/Prevotella species (Bac), (7) Clostridium coccoides group (Coer), (8) Clostridium leptum group (Cluster IV, sgClep), and (9) Mouse intestinal Bacteroides (MIB). No similarities were found between males and females regarding sex- and genotype-dependent microbiota distributions. Furthermore, a female Adnp +/– genotype associated decrease (contrasting male increase) was observed in the Lactobacillus group (Lacto). Significant correlations were discovered between specific bacterial group loads and open-field behavior as well as social recognition behaviors. In summary, we discovered ADNP deficiency associated changes in commensal gut microbiota compositions, a sex-dependent biomarker for the ADNP syndrome and beyond. Strikingly, we discovered rapidly detected NAP (CP201) treatment-dependent biomarkers within the gut microbiota.

Post-traumatic stress disorder (PTSD) is characterized by excessive emotion reactivity and diminished emotion regulation, corresponding to hyperactive amygdala and hypoactive ventro-medial pre-frontal cortex (vmPFC). Non-specific targeting of these process abnormalities might explain the currently moderate efficacy of therapeutic interventions in PTSD. This study introduces a randomized controlled trial (NCT02544971) with a neurofeedback (NF) intervention for PTSD patients, aimed at down-regulating amygdala activity. To target a disorder-specific process we applied individually-tailored trauma-related content as the learning feedback, allowing for personalized process-based NF. The effect of trauma content (Trauma-NF) was evaluated in comparison to a neutral feedback condition (Neutral-NF) and the intervention effect was controlled for by a No-NF condition. To scale-up applicability, neural activity was probed by an fMRI-inspired EEG model of the amygdala; Amygdala Electrical Finger Print (AmygEFP). All PTSD patients were assessed before and after treatment for clinical severity (primary: CAPS-5 and PCL) and neural-target engagement through amygdala fMRI-NF session. Results showed that patients in the treatment arm learned to volitionally down-regulate their AmygEFP signal and demonstrated reduction in PTSD symptoms, compared to the No-NF arm. Intriguingly, NF with personalized trauma interface presented the steepest NF learning over sessions and the largest clinical improvement (NNT=2.7). Superior down-regulation of amygdala BOLD signal during fMRI-NF following the intervention in the treatment arm, compared to No-NF, supported target engagement. These results demonstrate the potential of integrating self-neuromodulation with disorder-specific content to enhance clinical efficacy and specificity, and further establishes the AmygEFP as a mechanism-driven scalable NF intervention for PTSD.

The scientific community is increasingly concerned with the proportion of published "discoveries" that are not replicated in subsequent studies. The field of rodent behavioral phenotyping was one of the first to raise this concern, and to relate it to other methodological issues: the complex interaction between genotype and environment; the definitions of behavioral constructs; and the use of laboratory mice and rats as model species for investigating human health and disease mechanisms. In January 2015, researchers from various disciplines gathered at Tel Aviv University to discuss these issues. The general consensus was that the issue is prevalent and of concern, and should be addressed at the statistical, methodological and policy levels, but is not so severe as to call into question the validity and the usefulness of model organisms as a whole. Well-organized community efforts, coupled with improved data and metadata sharing, have a key role in identifying specific problems and promoting effective solutions. Replicability is closely related to validity, may affect generalizability and translation of findings, and has important ethical implications.

The early social environment can profoundly affect behavioral and physiological phenotypes. We investigated how male wild cavy offspring, whose mothers had either lived in a stable (SE) or an unstable social environment (UE) during pregnancy and lactation, differed in their anxiety-like behavior and stress responsiveness. At two different time points in life, we tested the offspring's anxiety-like behavior in a dark–light test and their endocrine reaction to challenge in a cortisol reactivity test. Furthermore, we analyzed whether individual traits remained stable over time. There was no effect of the early social environment on anxiety-like behavior and stress responsiveness. However, at an individual level, anxiety-like behavior was stable over time in UE- but not in SE-sons. Stress responsiveness, in turn, was rather inconsistent in UE-sons and temporally stable in SE-sons. Conclusively, we showed for the first time that the early social environment differentially shapes the stability of behavioral and endocrine traits. At first glance, these results may be surprising, but they can be explained by the different functions anxiety-like behavior and stress responsiveness have.

There has been a growing concern that preclinical research is failing to replicate experimental results. The problem is especially relevant to phenotyping genetically engineered mouse lines, a central strategy for discovering mammalian gene function and developing animal models of disease. We introduce a statistical method to estimate whether a single-laboratory discovery is likely to replicate in other laboratories, based on previously-estimated variability of genotype × laboratory interaction in large phenotyping databases. Future single-lab experiments can be used to further update the estimation, making the proposed method a true community effort. We validate the method by combining several datasets into the most inclusive analysis conducted to date of across-laboratory replication in mouse phenotyping, and estimate that with current single laboratory analysis 19%–41% of non-replicable phenotypic differences between genotypes are still published as “discoveries”. Applying our proposed method would reduce this rate to 3.3%–9%, close to the intended 0.05, both for testing and confidence intervals. This fileset includes the analysis results, the R and Sweave scripts to reproduce them, the input datasets, output tables with the estimated variances, the phenotypic measures transformations and the paper figure's source data.