@article{pmid42803345,

title = {Anhedonia as a transdiagnostic construct: implications for clinical practice and research},

author = {Isabella Di Matteo and Katie M Lavigne and Delphine Raucher-Chéné},

doi = {10.1192/bjp.2026.10793},

issn = {1472-1465},

year  = {2026},

date = {2026-09-01},

journal = {Br J Psychiatry},

pages = {1--3},

abstract = {Anhedonia, the diminished interest and/or capacity for pleasure, is increasingly recognised as a transdiagnostic symptom that spans mood, anxiety, neurodevelopmental and psychotic disorders. Rather than being specific to any single disorder, anhedonia reflects core dysfunction in reward processing and affect regulation, with broad implications for social and functional outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid42641351,

title = {Longitudinal inference of hippocampal volume and morphometric hippocampal-cortical covariance in psychosis},

author = {Jana F Totzek and Stephan Heckers and Lena Palaniyappan and Alexandra L Young and Jai L Shah and Ridha Joober and Ashok Malla and M Mallar Chakravarty and Katie M Lavigne and Martin Lepage},

doi = {10.1016/j.cortex.2026.07.007},

issn = {1973-8102},

year  = {2026},

date = {2026-08-01},

journal = {Cortex},

volume = {204},

pages = {154--169},

abstract = {Schizophrenia and related psychoses are characterized by reductions in hippocampal volume and morphometric hippocampal-cortical covariance. No study to date has inferred the temporal relationship between deviations in subfield and adjacent white matter volume and connectivity across psychosis. To explore this relationship, we sampled cross-sectional neuroimaging data from 174 patients and 120 non-clinical controls from two studies on first- and multi-episode psychosis. We applied Subtype and Stage Inference (SuStaIn), a machine learning algorithm combining clustering and disease progression modeling, to eight measures of hippocampal volume and morphometric hippocampal-cortical covariance (subfields and white matter regions per hemisphere) combined. We then performed in-depth SuStaIn analyses for 18 volumes (five subfields and four white matter regions per hemisphere) and 18 connectivity measures (graph-theory-based participation coefficient) separately. Some patients and controls showed no deviations (no increase or decrease) from control means in volume and covariance. For individuals who did show deviations, we inferred data-driven stages from deviations in volume toward covariance. In-depth SuStaIn analyses inferred three distinct volume patterns (intact volume, subfield-dominant, whiter matter-dominant) followed by one of four possible connectivity patterns (intact connectivity, whiter matter-dominant, subfield-dominant, subiculum-dominant). These subtypes showed significant differences in age, patient-control ratio and associations between data-driven stages and memory. In previous work, altered hippocampal volume and morphometric covariance distinctively precede cognitive and clinical symptoms of psychosis. While not all patients were assigned a staging pattern for hippocampal volume or connectivity, our findings might thus contribute to elucidating distinct hippocampal patterns underlying heterogeneous manifestations of psychosis for a subgroup of patients.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41687329,

title = {Chicken or the egg: The relationship between cognitive deficits and negative symptoms in first episode psychosis},

author = {Christy Au-Yeung and N Keita Christophe and Katie M Lavigne and Delphine Raucher-Chéné and Martin Lepage},

doi = {10.1016/j.schres.2026.02.008},

issn = {1573-2509},

year  = {2026},

date = {2026-04-01},

journal = {Schizophr Res},

volume = {290},

pages = {125--133},

abstract = {BACKGROUND: Cognitive impairments and negative symptoms (NS) represent an unmet therapeutic need in psychotic disorders. Both NS and cognitive impairments, are key predictors of functioning. Both also appear during the illness prodrome, highlighting their relevance to the etiology of psychosis. Cross-sectional and longitudinal links between NS and cognition have been established, suggesting a common underlying foundation. Thus, investigating the relationship between cognition and NS is particularly relevant, especially given the clinical fluctuations seen in the early stages of psychosis and the marked cognitive deficits present throughout the illness.nnOBJECTIVE: The following study aims to investigate the longitudinal relationship between NS and cognition and whether, over the first two years following psychosis onset, 1) NS influences cognition, 2) cognition influences NS, or 3) the relationship is bidirectional.nnMETHODS: We used data from the Recovery After an Initial Schizophrenia Episode Early Treatment Program (2010-2014). Participants underwent early treatment intervention, with assessments at baseline, 12, and 24 months using the Brief Assessment of Cognition in Schizophrenia and the Positive and Negative Syndrome Scale (n = 404). Random intercept cross-lagged panel models, which consider the temporal sequence of effects, were created to model the relationship between cognition and NS through the three timepoints over two years.nnRESULTS: We found a unidirectional relationship- declines in cognitive function predicted increased NS severity, while NS did not predict future cognitive function.nnCONCLUSION: This suggests that cognitive deficits may contribute to the development or exacerbation of NS. These findings underscore the role of cognition on NS trajectories and open new avenues for early psychosis treatments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid42094292,

title = {Digital tools for assessing bipolar disorder: A scoping review of the current landscape},

author = {Samantha Jacobson and Hannah Carling and Lisa Sarraf and Ethan Draper and Sarah Jacobson and Mélodie St-James and Chris Misiasz and Sianna Williamson and Elisabeth Thibaudeau and Geneviève Sauvé and Katie M Lavigne and Delphine Raucher-Chéné},

doi = {10.1016/j.nsa.2026.107004},

issn = {2772-4085},

year  = {2026},

date = {2026-01-01},

journal = {Neurosci Appl},

volume = {5},

pages = {107004},

abstract = {This scoping review synthesizes current evidence on the use of digital tools to assess bipolar disorder across clinical, behavioral, physiological, and psychosocial domains. An electronic search of Ovid, CINAHL, ClinicalTrials.gov, and OpenGrey, completed in March 2026, yielded 374 articles for full-text evaluation. Of which 214 studies met the inclusion criteria and were included in the final review. Most studies were conducted in the Global North, particularly the United States, Denmark, the United Kingdom, and Germany. Participant samples varied widely, although cohorts were typically comprised of young adults and predominantly female, with implications for the contexts in which digital markers have been evaluated across illness stages and demographic groups, and for the extent to which these findings can be generalized. Across studies, mood, sleep, and activity were the most frequently assessed domains, whereas physiological signals, cognitive performance, and communication-based features were less frequently examined, highlighting several underdeveloped but clinically relevant areas. Common measurement approaches included self-report questionnaires and ecological momentary assessment, whilst passive sensing approaches, such as accelerometry, geolocation, device-use metrics, speech features, and actigraphy, captured continuous indicators of sleep-wake cycles, mobility, and activity rhythms. The present synthesis reveals a clear temporal shift: since 2015, the field has transitioned from primarily active, self-report study designs toward multimodal, sensor-based methodologies, driven by advances in smartphones, wearable technologies, and sophisticated data-processing pipelines. Overall, this review highlights substantial progress in digital monitoring for bipolar disorder, with emerging tools offering ecologically valid, continuous assessment of core symptom dimensions. These developments lay essential groundwork for multimodal digital biomarkers with potential applications in early detection, tailored intervention, improved patient outcomes, and longitudinal care.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41124300,

title = {A multisite study of the overlap between symptoms and cognition in schizophrenia: Une étude multicentrique sur le chevauchement entre les symptômes et les troubles cognitifs chez les personnes atteintes de schizophrénie},

author = {Rafal M Skiba and Abhijit M Chinchani and Mahesh Menon and Martin Lepage and Katie M Lavigne and Ashok Malla and Ridha Joober and Joel O Goldberg and R Walter Heinrichs and David J Castle and Amy Burns and Michael W Best and Susan L Rossell and Sebastian Walther and Todd S Woodward},

doi = {10.1177/07067437251387565},

issn = {1497-0015},

year  = {2025},

date = {2025-10-01},

journal = {Can J Psychiatry},

pages = {7067437251387565},

abstract = {ObjectiveCognitive impairment is a core feature of schizophrenia spectrum disorders. Our previous study on a first-episode psychosis cohort showed that symptoms related to impoverished/disorganized communication and motor impoverishment predicted verbal and working memory scores, respectively. This study aimed to explore those predictors in people across the range of illness chronicity.MethodsWe employed iterative Constrained Principal Component Analysis (iCPCA) to investigate the relationship between 15 cognitive measures from the MATRICS battery, including processing speed, attention, working, verbal and nonverbal memory, reasoning, and problem-solving, and 27 Positive and Negative Syndrome Scale (PANSS) items in 198 outpatients from two sites in Australia and one in Canada. The iCPCA method was used to determine symptoms that reliably predict specific combinations of cognitive measures while controlling Type I errors.ResultsWe found that a verbal memory and learning component was predicted by the PANSS item , and a visual attention/working memory component was linked to the PANSS item .ConclusionsThese accord with our previous findings in an early psychosis sample, that is, negative symptoms of diminished expression are key predictors of cognitive abilities in schizophrenia. Namely, communication and motor impoverishments predicted lower scores on tests of verbal memory, learning, visual attention, and working memory. These findings may inform personalized treatment approaches targeting cognitive deficits and negative symptoms in schizophrenia.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40413924,

title = {From resting-state functional hippocampal centrality to functional outcome: An extended neurocognitive model of psychosis},

author = {Jana F Totzek and Jai L Shah and Alexandra L Young and Ashok Malla and Ridha Joober and Delphine Raucher-Chéné and Martin Lepage and Katie M Lavigne},

doi = {10.1016/j.psychres.2025.116538},

issn = {1872-7123},

year  = {2025},

date = {2025-08-01},

journal = {Psychiatry Res},

volume = {350},

pages = {116538},

abstract = {BACKGROUND: We previously proposed a neurocognitive model of psychosis in which reduced morphometric hippocampal-cortical connectivity precedes impaired episodic memory, social cognition, negative symptoms, and functional outcome. We provided support for this model in a patient subtype, and aimed to extend these findings to resting-state functional MRI to potentially explain the progression for a broader range of patients.nnMETHODS: We used a subsample of our previous analysis consisting of 54 patients with first-episode psychosis and 52 controls and applied the machine-learning algorithm Subtype and Stage Inference, which combines clustering and disease progression modeling, to the patient data for rs-functional hippocampal connectivity, episodic memory, social cognition, negative symptoms and functioning.nnRESULTS: We identified three subtypes, with Subtype 0 being unimpaired on the markers, Subtype 1 showing impaired hippocampal connectivity and episodic memory, and Subtype 2 showing impaired memory and a trend for impaired functioning. We identified similar progression patterns to our previously published morphometric results in functional MRI data (hippocampal dysconnectivity preceded cognition, symptoms, and functioning in one subtype and followed these alterations in another subtype). We further show that the impairments in our previously published and current findings across modalities do not necessarily overlap in patients, hinting towards an additive effect of morphometric and resting-state connectivity in explaining the neurocognitive underpinnings of this model.nnCONCLUSION: Our results provide an extension of our previous work and build the foundation for a multimodal neurocognitive model of psychosis, potentially elucidating this aspect of illness progression in psychosis for a broader range of patients.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40576457,

title = {How do negative symptoms and social cognitive impairments overlap? Clustering analyses on patients living with schizophrenia-spectrum disorder},

author = {Hannah Carling and Elisabeth Thibaudeau and Geneviève Sauvé and Danielle Penney and Katie M Lavigne and Martin Lepage and Delphine Raucher-Chéné},

doi = {10.1093/scan/nsaf061},

issn = {1749-5024},

year  = {2025},

date = {2025-06-01},

journal = {Soc Cogn Affect Neurosci},

abstract = {Negative symptoms and social cognition (SC) are intertwined in schizophrenia spectrum disorders (SSD) but the structure of this interaction is not yet fully understood. We employed cluster analyses to advance our understanding of the relationship between negative symptom severity and SC. We sought to identify discrete groups of patients as a function of two factors of negative symptoms-Motivation and Pleasure (MAP) and Expressivity (EXP) - and two domains of SC: emotion recognition (ER) and theory of mind (ToM). We conducted two cluster analyses to determine data-driven subgroups using two independent samples of SSD participants. The first conducted with an open dataset (n = 296) and the second with a local sample (n = 138), to assess replicability. The first cluster analysis revealed a 3-cluster solution. Both analyses highlighted distinct profiles: a 'Relatively Preserved' Profile; a 'Combined Impairment' profile, with high negative symptoms and impaired ER and ToM; and a 'MAP' profile, with high MAP symptoms, some EXP symptoms, and slightly to moderately impaired ER and ToM. Reducing the heterogeneity in clinical presentations of SSD patients on these dimensions of negative symptoms and social cognition provides relevant information that could contribute to a more effective selection of interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40320230,

title = {Longitudinal clinical outcomes based on cognitive and hippocampal clusters of first episode psychosis},

author = {Lucas A Ronat and Delphine Raucher-Chéné and Katie M Lavigne and Mallar Chakravarty and Ridha Joober and Ashok Malla and Jai Shah and Martin Lepage},

doi = {10.1016/j.pnpbp.2025.111392},

issn = {1878-4216},

year  = {2025},

date = {2025-06-01},

journal = {Prog Neuropsychopharmacol Biol Psychiatry},

volume = {139},

pages = {111392},

abstract = {BACKGROUND: In first episode psychosis (FEP), cognitive impairments are core features contributing to clinical and functional heterogeneity. Significant impairment indicates greater clinical severity throughout the course of the illness, particularly for negative symptoms. Hippocampal volume is smaller in FEP than in healthy controls (notably subfields like Cornu Ammonis 1-3 and subiculum), and is related to cognitive impairments and negative symptoms. The aim of this study was to compare the clinical and functional trajectories of FEP subgroups as a function of cognitive performance and hippocampal volumes.nnMETHODS: One hundred FEP patients and sixty healthy controls initially assessed using the CogState research battery, underwent 3 T MRI to extract hippocampal subfields and adjacent structures using the MAGeT brain algorithm. Clinical assessments were carried out for negative (Motivational and Pleasure - MAP, and diminished expression - EXP) and depressive symptoms, and global functioning. Measurements were taken at 4 time points (3, 9, 15, 21 months following program entry). Based on available first timepoint standardized cognitive and hippocampal features, using healthy controls as reference, clusters were determined by a hierarchical ascending classification. Their clinical and functional longitudinal trajectories were analyzed using linear mixed-effects models.nnRESULTS: Three baseline clusters were revealed: normal-range hippocampal volume with low attention, working and verbal memory (FEP 0), small hippocampus with low verbal memory and social cognition (FEP 1), and large hippocampus with low verbal memory (FEP 2). At baseline, the clusters did not differ on symptoms severity and global functioning. Longitudinally, MAP, EXP and depressive symptoms decreased over time in FEP 0. Global functioning improved in FEP 0 and FEP 1, while FEP 2 was clinically and functionally stable over time. Longitudinal inter-group comparisons did not yield any significant differences.nnCONCLUSION: The clusters were dissociated between hippocampus and cognition, but their trajectories suggest the importance of hippocampal integrity in the clinical and/or functional outcome. Future studies are needed to understand intervention efficiency depending on hippocampal integrity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40015233,

title = {Association of anticholinergic burden with hippocampal subfields volume in first-episode psychosis},

author = {Agnès Belkacem and Katie M Lavigne and Delphine Raucher-Chéné and Carolina Makowski and Mallar Chakravarty and Ridha Joober and Ashok Malla and Jai Shah and Martin Lepage},

doi = {10.1016/j.pscychresns.2025.111968},

issn = {1872-7506},

year  = {2025},

date = {2025-04-01},

journal = {Psychiatry Res Neuroimaging},

volume = {348},

pages = {111968},

abstract = {Polypharmacy is relatively common in early psychosis, but little attention has been paid to the anticholinergic burden of medication use (the cumulative effect of medications that block the cholinergic system). Evidence suggests that anticholinergic burden is associated with cognitive deficits and that hippocampal dysfunction may be involved in those impairments. We aimed to examine this association in a cohort of patients with first-episode psychosis. We hypothesized that patients with the highest burden would experience a more significant reduction in hippocampal volume compared to those with low burden and healthy controls, both at baseline (3 months) and at month 12. Patients (n = 82; low burden [n = 64] and high burden [n = 18], defined by a Drug Burden Index cut-off of 1) followed at the PEPP-Montreal clinic, and controls (n = 55) completed a 3T MRI at both timepoints. After controlling for antipsychotic dosage at both timepoints, results at baseline and over time revealed a greater reduction in left fimbria volumes in high-burden patients compared to low-burden patients and controls. Overall, the associations observed between high anticholinergic burden and hippocampal volume provide further evidence for considering this dimension when prescribing medication in early psychosis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40116708,

title = {Mapping Insight Dimensions and Symptom Dynamics in Schizophrenia: A Data-Driven Network Approach: Cartographie des dimensions d'insight et de la dynamique symptomatique dans la schizophrénie: une approche par réseau fondée sur les données},

author = {Jesse Rae and Katie M Lavigne and Geneviève Sauvé and Martin Lepage and Delphine Raucher-Chéné},

doi = {10.1177/07067437251329074},

issn = {1497-0015},

year  = {2025},

date = {2025-04-01},

journal = {Can J Psychiatry},

volume = {70},

number = {4},

pages = {301--311},

abstract = {ObjectivesPatients with  (SSD) present with cognitive, behavioral, and emotional difficulties. Affected individuals often exhibit poor insight into aspects of their illness, such as awareness of the illness itself or the need for treatment, which can hinder treatment adherence and complicate clinical outcomes. This study aimed to investigate the relationships between clinical symptoms and dimensions of insight in SSD using a network approach, which captures direct and indirect relationships among variables. We hypothesized that illness awareness would correlate negatively with positive symptoms and positively with depressive symptoms, and that positive symptoms would have the strongest influence on the network.MethodsData were collected from 142 individuals diagnosed with SSD. Insight was measured using the Birchwood Insight Scale (IS) across three dimensions: illness awareness, symptom re-labelling, and awareness of the need for treatment. Symptoms were evaluated using the Scale for the Assessment of Positive Symptoms, the Scale for the Assessment of Negative Symptoms, the Calgary Depression Scale and the Hamilton Anxiety Scale. Network analysis was employed to explore interconnections (edges) between variables (nodes) and identify influential variables through centrality measures (strength, betweenness, closeness).ResultsA significant positive connection was found between illness awareness and depressive symptoms. Anxiety and depressive symptoms were identified as the most central and influential variables within the network. Treatment awareness showed greater centrality than illness awareness, indicating this dimension's potential importance in influencing symptom dynamics in a clinical profile.ConclusionsAnalyzing a more extensive network that includes treatment adherence and cognitive domains affected in SSD could enhance and validate the understanding of the cascading effects of symptoms and insight dimensions, allowing for more tailored treatments.Plain Language Summary TitleInterconnections between levels of awareness and clinical symptoms in schizophrenia and related disorders.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40233365,

title = {iCogCA to Promote Cognitive Health Through Digital Group Interventions for Individuals Living With a Schizophrenia Spectrum Disorder: Protocol for a Nonrandomized Concurrent Controlled Trial},

author = {Christy Au-Yeung and Helen Thai and Michael Best and Christopher R Bowie and Synthia Guimond and Katie M Lavigne and Mahesh Menon and Steffen Moritz and Myra Piat and Geneviève Sauvé and Ana Elisa Sousa and Elisabeth Thibaudeau and Todd S Woodward and Martin Lepage and Delphine Raucher-Chéné},

doi = {10.2196/63269},

issn = {1929-0748},

year  = {2025},

date = {2025-04-01},

journal = {JMIR Res Protoc},

volume = {14},

pages = {e63269},

abstract = {BACKGROUND: Cognitive impairments are a key aspect of schizophrenia spectrum disorders (SSDs), significantly affecting clinical and functional outcomes. The COVID-19 pandemic has heightened concerns about mental health services and cognitive stimulation opportunities. Despite evidence-based interventions like action-based cognitive remediation (ABCR) and metacognitive training (MCT), a research-to-practice gap exists in their application across mental health settings.nnOBJECTIVE: The iCogCA study aims to address this gap by implementing digital ABCR and MCT through a national Canadian collaborative effort using digital psychological interventions to enhance cognitive health in SSDs.nnMETHODS: The study involves 5 Canadian sites, with mental health care practitioners trained digitally through the E-Cog platform, which was developed by our research group. Over 2.5 years, participants with SSDs will undergo pre- and postintervention assessments for clinical symptoms, cognition, and functioning. Each site will run groups annually for both ABCR and MCT, totaling ~390 participants. A nonrandomized concurrent controlled design will assess effectiveness design, in which one intervention (eg, ABCR) acts as the active control for the other (eg, MCT) and vice versa, comparing cognitive and clinical outcomes between the interventions using generalized linear mixed effect modeling. Implementation strategy evaluation will consider the digital platform's efficacy for mental health care practitioners' training, contextual factors influencing implementation, and sustainability, using descriptive statistics for quantitative data and thematic analysis for qualitative data.nnRESULTS: A pilot pragmatic trial has been conducted previously at the Montreal site, evaluating 3 early implementation outcomes: acceptability, feasibility, and engagement. Patient and therapist acceptability was deemed as high and feasible (21/28, 75% of recruited service users completed therapy, rated feasible by therapists). Technology did not appear to significantly impede program participation. Therapist-rated levels of engagement were also satisfactory. In the ongoing study, recruitment is underway (114 participants recruited as of winter 2024), and intervention groups have been conducted at all sites, with therapists receiving training via the E-Cog learning platform (32 enrolled as of winter 2024).nnCONCLUSIONS: At least 3 significant innovations will stem from this project. First, this national effort represents a catalyst for the use of digital technologies to increase the adoption of evidence-based interventions and will provide important results on the effectiveness of digitally delivered ABCR and MCT. Second, the results of the implementation component of this study will generate the expertise needed to inform the implementation of similar initiatives. Third, the proposed study will introduce and validate our platform to train and supervise mental health care practitioners to deliver these interventions, which will then be made accessible to the broader mental health community.nnTRIAL REGISTRATION: ClinicalTrials.gov NCT05661448; https://clinicaltrials.gov/study/NCT05661448.nnINTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63269.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39986368,

title = {White matter integrity and verbal memory following a first episode of psychosis: A longitudinal study},

author = {Joseph Ghanem and Jana F Totzek and Charlie Henri-Bellemare and Delphine Raucher-Chéné and Gregory Kiar and Raihaan Patel and M Mallar Chakravarty and Jai L Shah and Ridha Joober and Ashok Malla and Martin Lepage and Katie M Lavigne},

doi = {10.1016/j.pnpbp.2025.111294},

issn = {1878-4216},

year  = {2025},

date = {2025-03-01},

journal = {Prog Neuropsychopharmacol Biol Psychiatry},

volume = {137},

pages = {111294},

abstract = {Psychotic disorders are heterogeneous disorders for which there is evidence of structural and functional brain abnormalities. The role of white matter integrity, often measured via Fractional Anisotropy (FA), has played a controversial role in individuals with a first episode of psychosis (FEP). Similarly, some FEP studies have observed that higher FA is associated with better verbal memory, but others failed to find such an association. Studying the early stages of psychosis represents a promising avenue to overcome previous confounding factors and characterize the disease in its early clinical stages. Eighty individuals with a FEP were recruited from a specialized early intervention program for psychosis alongside 55 non-clinical controls from the community matched for age and sex. Both groups were followed and scanned 4 times: at baseline (within 3 months after program entry), 6 months, 12 months, and 18 months. Tract-Based Spatial Statistics (TBSS) were used on 3.0 Tesla diffusion-weighted images to extract fractional anisotropy values for white matter regions of interest in accordance with the John Hopkins University white-matter tractography atlas. The analysis revealed no significant main effect of group or time, and no significant associations between FA and verbal memory. Overall, differences in FA are small early in the course of illness and longer follow-up periods may be required to identify possible changes during a critical intervention window.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41116811,

title = {E-Cog: An online training platform for cognitive health interventions based on the ADDIE model},

author = {Ana Elisa Sousa and Caroline Dakoure and Christy Au-Yeung and Katie M Lavigne and Delphine Raucher-Chéné and Christopher R Bowie and Steffen Moritz and Martin Lepage and Geneviève Sauvé},

doi = {10.1177/20552076251381249},

issn = {2055-2076},

year  = {2025},

date = {2025-01-01},

journal = {Digit Health},

volume = {11},

pages = {20552076251381249},

abstract = {BACKGROUND: Online learning is an accessible and cost-effective solution to deliver training in healthcare. In-person training has long supported mental health practitioners in delivering cognitive health interventions, however for many underserved populations, significant barriers to accessing standardized, evidence-based training for such treatments remain. The E-Cog platform aims to bridge this implementation gap, delivering accessible and engaging remote training that maintains the quality of in-person training.nnOBJECTIVES: Describe the design, development, and implementation of E-Cog, an innovative online training platform for two cognitive health interventions, within the context of a Canadian multisite implementation trial for individuals diagnosed with psychosis. Additionally, examine the feasibility and acceptability of the ADDIE educational framework for e-learning development.nnMETHODS: Following the ADDIE model's five phases (analysis, design, development, implementation, and evaluation), we developed our training platform and its curriculum of two cognitive health online certifications: Action-Based Cognitive Remediation and Metacognitive Training. This protocol describes the first four phases of our work and reports on the feasibility and acceptability of the ADDIE model.nnCONCLUSIONS: E-Cog has been implemented to deliver remote, asynchronous training that maintains in-person quality standards and shows potential for cost-effectiveness. Successful uptake of mental health practitioners ( = 28) was observed during the first two years of implementation. ADDIE was perceived as feasible and acceptable as a framework for e-learning content, depending on flexibility to adapt its structure to research challenges and constraints. Next steps include a qualitative assessment of E-Cog's usability and impact on intervention delivery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38605172,

title = {Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome},

author = {Jana F Totzek and M Mallar Chakravarty and Ridha Joober and Ashok Malla and Jai L Shah and Delphine Raucher-Chéné and Alexandra L Young and Dennis Hernaus and Martin Lepage and Katie M Lavigne},

doi = {10.1038/s41380-024-02549-x},

issn = {1476-5578},

year  = {2024},

date = {2024-10-01},

journal = {Mol Psychiatry},

volume = {29},

number = {10},

pages = {2929--2938},

abstract = {Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38924940,

title = {Overlap between individual differences in cognition and symptoms of schizophrenia},

author = {Rafal M Skiba and Abhijit M Chinchani and Mahesh Menon and Martin Lepage and Katie M Lavigne and Ashok Malla and Ridha Joober and Joel O Goldberg and R Walter Heinrichs and David J Castle and Amy Burns and Michael W Best and Susan L Rossell and Sebastian Walther and Todd S Woodward},

doi = {10.1016/j.schres.2024.06.010},

issn = {1573-2509},

year  = {2024},

date = {2024-08-01},

journal = {Schizophr Res},

volume = {270},

pages = {220--228},

abstract = {BACKGROUND: Neurocognitive impairment is a core feature of schizophrenia spectrum disorders (SSDs), and the relationship between cognition and symptoms in SSDs has been widely researched. Negative symptoms are related to a wide range of cognitive impairments; however, the aspects of negative symptoms that underpin this relationship have yet to be specified.nnSTUDY DESIGN: We used iterative Constrained Principal Component Analysis (iCPCA) to explore the relationship between 18 cognitive measures (including processing speed, attention, working, spatial and verbal memory and executive functions) and 46 symptoms in schizophrenia at the individual item level while minimizing the risk of Type I errors. ICPCA was conducted on a sample of SSD patients in the early stages of psychiatric treatment (n = 121) to determine the components of cognition overlapping with symptoms measured by the Scale for the Assessment of Negative Symptoms (SANS) and the Scale for the Assessment of Positive Symptoms (SAPS).nnRESULTS: We found that a verbal memory component was associated with items from SANS and SAPS related to impoverished and disorganized emotional communication, language, and thought. In contrast, a working memory component was associated with SANS items related to motor system impoverishment.nnCONCLUSIONS: The iCPCA allowed us to explore the associations between individual items, optimized to understand the overlap between symptoms and cognition. The specific symptoms linked to verbal and working memory impairments imply distinct brain networks, which further investigation may lead to our deeper understanding of the illness and the development of treatment methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38428118,

title = {Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities},

author = {Farida Zaher and Mariama Diallo and Amélie M Achim and Ridha Joober and Marc-André Roy and Marie-France Demers and Priya Subramanian and Katie M Lavigne and Martin Lepage and Daniela Gonzalez and Irnes Zeljkovic and Kristin Davis and Michael Mackinley and Priyadharshini Sabesan and Shalini Lal and Alban Voppel and Lena Palaniyappan},

doi = {10.1016/j.schres.2024.02.036},

issn = {1573-2509},

year  = {2024},

date = {2024-04-01},

journal = {Schizophr Res},

volume = {266},

pages = {205--215},

abstract = {Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37956787,

title = {Transdiagnostic cognitive biases in psychiatric disorders: A systematic review and network meta-analysis},

author = {Katie M Lavigne and Jiaxuan Deng and Delphine Raucher-Chéné and Adèle Hotte-Meunier and Chloe Voyer and Lisa Sarraf and Martin Lepage and Geneviève Sauvé},

doi = {10.1016/j.pnpbp.2023.110894},

issn = {1878-4216},

year  = {2024},

date = {2024-02-01},

journal = {Prog Neuropsychopharmacol Biol Psychiatry},

volume = {129},

pages = {110894},

abstract = {Psychiatric disorders are characterized by cognitive deficits, which have been proposed as a transdiagnostic feature of psychopathology ("C" factor). Similarly, cognitive biases (e.g., in attention, memory, and interpretation) represent common tendencies in information processing that are often associated with psychiatric symptoms. However, the question remains whether cognitive biases are also transdiagnostic or are specific to certain psychiatric disorders/symptoms. The current systematic review sought to address whether the proposed "C" factor of transdiagnostic cognitive dysfunction in psychopathology can be extended to cognitive biases. Overall, 31 studies comprising 4401 participants (2536 patients, 1865 non-clinical controls) met inclusion criteria, assessing 19 cognitive biases across 20 diagnostic categories, with most studies focusing on interpretation (k = 22) and attention (k = 11) biases and only 2 assessing memory biases. Traditional meta-analyses found a moderate effect size (g = 0.32) for more severe cognitive biases in all patients relative to non-clinical controls, as well as small but significant associations between interpretation biases and transdiagnostic symptom categories (general psychopathology: r = 0.20, emotion dysfunction: r = 0.17, psychotic symptoms: r = 0.25). Network meta-analyses revealed significant patient versus non-clinical control differences on attention and interpretation biases across diagnoses, as well as significant differences between diagnoses, with highest severity in panic disorder for attention biases and obsessive-compulsive disorder for interpretation biases. The current findings extend the big "C" interpretation of transdiagnostic cognitive dysfunction in psychiatric disorders to cognitive biases and transdiagnostic symptom dimensions. Results also suggest that while the presence of cognitive biases is transdiagnostic, bias severity differs across diagnoses, as in traditional neurocognitive deficits.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37847817,

title = {Exploring the Relationship Between Suicidality and Persistent Negative Symptoms Following a First Episode of Psychosis},

author = {Joseph Ghanem and Massimiliano Orri and Laura Moro and Katie M Lavigne and Delphine Raucher-Chéné and Ashok Malla and Ridha Joober and Martin Lepage},

doi = {10.1093/schbul/sbad146},

issn = {1745-1701},

year  = {2023},

date = {2023-10-01},

journal = {Schizophr Bull},

abstract = {BACKGROUND AND HYPOTHESIS: Suicide is a leading cause of death in first-episode psychosis (FEP), with an elevated risk during the first year following illness onset. The association between negative symptoms and suicidality remains contentious. Some studies suggest that negative symptoms may be associated with lower suicidality, while others fail to find an association between the two. No previous studies have specifically investigated suicidality in Persistent Negative Symptoms (PNS) and its associated subgroups.nnSTUDY DESIGN: In a large cohort of FEP patients (n = 515) from an early intervention service, we investigated suicidality in those with PNS, secondary PNS (ie, sPNS; PNS with clinical-level positive, depressive, or extrapyramidal symptoms), and non-PNS (all other patients) over 24 months. Patients were categorized into PNS groups based on symptoms from month 6 to month 12, and suicidality was evaluated using the Brief Psychiatric Rating Scale (BPRS).nnSTUDY RESULTS: Covarying for age and sex, we found that sPNS had higher suicidality relative to PNS and non-PNS throughout the 24-month period, but PNS and non-PNS did not differ. These differences were maintained after adjusting for depressive symptoms.nnCONCLUSION: We observed that PNS did not significantly differ from non-PNS. However, we identified sPNS as a group with elevated suicidality above and beyond depression, suggesting that sPNS would benefit from targeted intervention and that PNS categorization identifies a subgroup for whom negative symptoms are not associated with lower suicidality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37484195,

title = {C-Reactive protein and cognition: Mediation analyses with brain morphology in the UK Biobank},

author = {Daniel Mendelson and Romina Mizrahi and Martin Lepage and Katie M Lavigne},

doi = {10.1016/j.bbih.2023.100664},

issn = {2666-3546},

year  = {2023},

date = {2023-08-01},

journal = {Brain Behav Immun Health},

volume = {31},

pages = {100664},

abstract = {Cognitive impairments and abnormal immune activity are both associated with various clinical disorders. The association between C-Reactive protein (CRP), a marker associated with inflammation, and cognitive performance remains unclear. Further, mechanisms potentially linking CRP to cognition are not yet established. Brain structure may well mediate this relationship: immune processes play crucial roles in shaping and maintaining brain structure, with brain structure and function driving cognition. The United Kingdom Biobank (UKBB) is a large cohort study with extensive assessments, including high-sensitivity serum CRP levels, brain imaging, and various cognitive tasks. With data from 39,200 UKBB participants, we aimed first to determine the relationship between CRP and cognitive performance, and second, to assess metrics of brain morphology as potential mediators in this relationship. Participants were aged 40 to 70 at initial assessment and were mostly Caucasian. After accounting for potential covariates (e.g., age, sex, medical diagnoses, use of selective-serotonin reuptake inhibitors), we found CRP levels to have small, negative associations with fluid intelligence ( = -0.03, 95%CI[-0.05,-0.02], (14381) = -3.62,  = .004), and numeric memory ( = -0.03, 95%CI[-0.05,-0.01], (14366) = -3.31,  = .007). We found no evidence of brain morphology mediating these relationships (all || < 0.001, all  > .55). Our findings from this large sample suggest that serum-assessed CRP is of marginal importance for cognitive performance in mid-to-late aged Caucasians; the small effect sizes of statistically significant associations provide context to previous inconsistent results. The seeming lack of involvement of brain morphology suggests that other brain metrics (e.g., connectivity, functional activation) may be more pertinent to this relationship. Future work should also consider CRP levels measured in the central nervous system and/or other cytokines that may better predict cognitive performance in this population.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37557917,

title = {Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants},

author = {Crystal C Yang and Jana F Totzek and Martin Lepage and Katie M Lavigne},

doi = {10.1093/cercor/bhad286},

issn = {1460-2199},

year  = {2023},

date = {2023-08-01},

journal = {Cereb Cortex},

abstract = {There is robust evidence for sex differences in domain-specific cognition, where females typically show an advantage for verbal memory, whereas males tend to perform better in spatial memory. Sex differences in brain connectivity are well documented and may provide insight into these differences. In this study, we examined sex differences in cognition and structural covariance, as an index of morphometric connectivity, of a large healthy sample (n = 28,821) from the UK Biobank. Using T1-weighted magnetic resonance imaging scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of structural covariance, hypothesizing that sex-related differences in brain network global efficiency, or overall covariance, would underlie cognitive differences. As predicted, females demonstrated better verbal memory and males showed a spatial memory advantage. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males showed higher global efficiency, as well as higher regional covariance (nodal strengths) in both hemispheres relative to females. Furthermore, higher global efficiency in males mediated sex differences in verbal memory and processing speed. Findings contribute to an improved understanding of how biological sex and differences in cognition are related to morphometric connectivity as derived from graph-theoretic methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}