Local connectivity and synaptic dynamics in mouse and human neocortex.

We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Synaptic properties are heterogeneous in most subclass-to-subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3

Personal resources and depression in schizophrenia: The role of self-esteem, resilience and internalized stigma

Depression in schizophrenia represents a challenge from a diagnostic, psychopathological and therapeutic perspective. The objective of this study is to test the hypothesis that resilience and self-stigma affect depression severity and to evaluate the strength of their relations in 921 patients with schizophrenia. A structural equation model was tested where depression is hypothesized as affected by resilience, internalized stigma, gender and negative symptoms, with the latter two variables used as exogenous covariates and the former two as mediators. The analysis reveals that low resilience, high negative symptoms, female gender were directly associated with depression severity, and internalized stigma acted only as a mediator between avolition and resilience, with similar magnitude. The cross-sectional study design and the variable selection limit the generalizability of the study results. The model supports a complex interaction between personal resources and negative symptoms in predicting depression in schizophrenia. The clinical implication of these findings is that personal resources could be a significant target of psychosocial treatments

Familial aggregation of MATRICS Consensus Cognitive Battery scores in a large sample of outpatients with schizophrenia and their unaffected relatives

Background: The increased use of the MATRICS Consensus Cognitive Battery (MCCB) to investigate cognitive dysfunctions in schizophrenia fostered interest in its sensitivity in the context of family studies. As various measures of the same cognitive domains may have different power to distinguish between unaffected relatives of patients and controls, the relative sensitivity of MCCB tests for relativeâ\u80\u93control differences has to be established. We compared MCCB scores of 852 outpatients with schizophrenia (SCZ) with those of 342 unaffected relatives (REL) and a normative Italian sample of 774 healthy subjects (HCS). We examined familial aggregation of cognitive impairment by investigating within-family prediction of MCCB scores based on probandsâ\u80\u99 scores. Methods: Multivariate analysis of variance was used to analyze group differences in adjusted MCCB scores. Weighted least-squares analysis was used to investigate whether probandsâ\u80\u99 MCCB scores predicted REL neurocognitive performance. Results: SCZ were significantly impaired on all MCCB domains. REL had intermediate scores between SCZ and HCS, showing a similar pattern of impairment, except for social cognition. Proband's scores significantly predicted REL MCCB scores on all domains except for visual learning. Conclusions: In a large sample of stable patients with schizophrenia, living in the community, and in their unaffected relatives, MCCB demonstrated sensitivity to cognitive deficits in both groups. Our findings of significant within-family prediction of MCCB scores might reflect disease-related genetic or environmental factors

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services

Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at s\sqrt{s} = 13 TeV

International audienceThe identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10\GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected in 2018 with the CMS experiment at the CERN LHC

Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at s\sqrt{s} = 13 TeV

International audienceThe identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10\GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected in 2018 with the CMS experiment at the CERN LHC

Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at s\sqrt{s} = 13 TeV

International audienceThe identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10\GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected in 2018 with the CMS experiment at the CERN LHC

Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at s= \sqrt{s}= 13 TeV

The identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10 GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-mass energy of $\sqrt{s}=$ 13 TeV collected in 2018 with the CMS experiment at the CERN LHC.The identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10\GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected in 2018 with the CMS experiment at the CERN LHC

Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at s\sqrt{s} = 13 TeV

The identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10\GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected in 2018 with the CMS experiment at the CERN LHC

Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at s\sqrt{s} = 13 TeV

International audienceThe identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10\GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected in 2018 with the CMS experiment at the CERN LHC