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Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD’s clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool’s recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.

While there is substantial agreement on the diagnostic criteria for autism spectrum disorder, it is also acknowledged that it has a broad range of clinical presentations. This can complicate the diagnostic process and aggravate the choice of the most suitable rehabilitative strategy for each child. Attentional difficulties are among the most frequently reported comorbidities in autism spectrum disorder. We investigated the role of SNAP-25 polymorphisms. Synaptosome-associated protein 25 (SNAP25) is a presynaptic membrane-binding protein; it plays a crucial role in neurotransmission and has already been studied in numerous psychiatric disorders. It was also seen to be associated with hyperactivity in children with autism spectrum disorder. We collected clinical, behavioral and neuropsychological data on 41 children with a diagnosis of autism spectrum disorder, and then genotyped them for five single-nucleotide polymorphisms of SNAP-25. Participants were divided into two groups according to the Autism Diagnostic Observation Schedule (ADOS-2) Severity Score. In the group with the highest severity score, we found significant associations of clinical data with polymorphism rs363050 (A/G): children with the GG genotype had lower total IQ, more severe autistic functioning and more attentional difficulties. Our research could be the starting point for outlining a possible endophenotype among patients with autism spectrum disorder who are clinically characterized by severe autistic functioning and significant attentional difficulties.

IntroductionPathophysiological etiology of schizophrenia remains unclear due to the heterogeneous nature of its biological and clinical manifestations. Dysfunctional communication among large-scale brain networks and hub nodes have been reported. In this study, an exploratory approach was adopted to evaluate the dysfunctional connectome of brain in schizophrenia.MethodsTwo hundred adult individuals with schizophrenia and 200 healthy controls were recruited from Taipei Veterans General Hospital. All subjects received functional magnetic resonance imaging (fMRI) scanning. Functional connectivity (FC) between parcellated brain regions were obtained. Pair-wise brain regions with significantly different functional connectivity among the two groups were identified and further analyzed for their concurrent ratio of connectomic differences with another solitary brain region (single-FC dysfunction) or dynamically interconnected brain network (network-FC dysfunction).ResultsThe right thalamus had the highest number of significantly different pair-wise functional connectivity between schizophrenia and control groups, followed by the left thalamus and the right middle frontal gyrus. For individual brain regions, dysfunctional single-FCs and network-FCs could be found concurrently. Dysfunctional single-FCs distributed extensively in the whole brain of schizophrenia patients, but overlapped in similar groups of brain nodes. A dysfunctional module could be formed, with thalamus being the key dysfunctional hub.DiscussionThe thalamus can be a critical hub in the brain that its dysfunctional connectome with other brain regions is significant in schizophrenia patients. Interconnections between dysfunctional FCs for individual brain regions may provide future guide to identify critical brain pathology associated with schizophrenia.

Aim. To identify subgroups of patients with primary osteoarthritis of the hip joint (pHOA) with similar imaging and laboratory findings, disease evolution, and response to conventional therapies. Methods. We performed further statistical analyses on patient data from two published, double-blind, randomized, and placebo-controlled studies (DB-RCTs), which examined the effects of intra-articular corticosteroids (ia-CSs), hyaluronic acid (ia-HA)—KИ-109-3-0008/14.01.2014, and intravenous bisphosphonates (iv-BPs) -KИ- 109-3-0009/14.01.2014 compared to the country’s standard pHOA therapy. The data span an 8-year follow-up of 700 patients with pHOA, including: 1. Clinical parameters (WOMAC-A, B, C, and T; PtGA). 2. Laboratory markers (serum calcium and phosphate levels; 25-OH-D and PTH, markers for bone sCTX-I and cartilage uCTX-II turnover). 3. Radiological indicators: X-ray stage (Kellgren-Lawrence (K/L) and model (Bombelli/OOARSI), width (mJSW), speed (JSN mm/year), and zone of maximum narrowing of the joint space (max-JSN)—determining the type of femoral head migration (FHM). 4. DXA indicators: bone geometry (HAL; NSA; and MNW); changes in regional and total bone mineral density (TH-BMD, LS-BMD, and TB-BMD). 5. Therapeutic responses (OARSI/MCII; mJSW; JSNmm/yearly) to different drug regimens (iv-BP -zoledronic acid (ZA/-5 mg/yearly for 3 years)); ia-CS 40 mg methylprednisolone acetate, twice every 6 months; and ia-HA with intermediate molecular weight (20 mg/2 mL × 3 weekly applications, two courses every 6 months) were compared to standard of care therapy (Standard of Care/SC/), namely D3-supplementation according to serum levels (20–120 ng/mL; target level of 60 ng/mL), simple analgesics (paracetamol, up to 2.0 g/24 h), and physical exercises. The abovementioned data were integrated into a non-supervised hierarchical agglomerative clustering analysis (NHACA) using Ward’s linkage method and the squared Euclidean distance to identify different endophenotypes (EFs). Univariate and multivariate multinomial logistic regression analyses were performed to determine the impact of sex and FHM on clinical and radiographic regression of pHOA. Results. A baseline cluster analysis using incoming (M0) patient data identified three EFs: hypertrophic H-HOA, atrophic A-HOA, and intermediate I-HOA. These EFs had characteristics that were similar to those of patients grouped by radiographic stage and pattern (‘H’-RPs, ‘I’-RPs, and ‘A’-RPs), p < 0.05). The repeated cluster analysis of M36 data identified four EF pHOAs: 1. Hypertrophic (slow progressors, the influence of the type of femoral head migration (FHM) outweighing the influence of sex on progression), progressing to planned total hip replacement (THR) within 5 (K/LIII) to 10 (K/LII) years. 2. Intermediate (sex is more important than the FHM type for progression) with two subgroups: 2#: male-associated (slow progressors), THR within 4 (K/LIII) to 8 years. (K/LII). 2* Female-associated (rapid progressors), THR within 3 (K/LIII) to 5 (K/LII) years. 3. Atrophic (rapid progressors; the influence of FHM type outweighs that of sex), THR within 2 (K/LIII) to 4 (K/LII) years. Each EF, in addition to the patient’s individual progression rate, was also associated with a different response to the aforementioned therapies. Conclusions. Clinical endophenotyping provides guidance for a personalized approach in patients with pHOA, simultaneously assisting the creation of homogeneous patient groups necessary for conducting modern genetic and therapeutic scientific studies.

Abstract Background Autism-spectrum disorder is a neurodevelopmental disorder with heterogeneity in etiopathogenesis and clinical presentation. Neuroanatomical and neurophysiological abnormalities may represent neural endophenotypes for autism spectrum disorders which may help identify subgroups of patients seemingly similar in clinical presentation yet different in their pathophysiological underpinnings. Furthermore, a thorough understanding of the pathophysiology of disease can pave the way to effective treatments, prevention, and prognostic predictions. The aim of this review is to identify the predominant neural endophenotypes in autism-spectrum disorder. The evidence was researched at the following electronic databases: Pubmed, PsycINFO, Scopus, Web of Science, and EMBASE. Results Enlarged brain, especially frontotemporal cortices have been consistently reported by structural neuroimaging, whereas functional neuroimaging has revealed frontotemporal dysconnectivity. Conclusions Regrettably, many of these findings have not been consistent. Therefore, translating these findings into neural endophenotype is by far an attempt in its budding stage. The structural and functional neuroimaging changes may represent neural endophenotypes unique to autism-spectrum disorder. Despite inconsistent results, a clinically meaningful finding may require combined efforts of autism-spectrum-disorder researchers focused on different aspects of basic, genetic, neuroimaging, and clinical research.

Type 2 diabetes mellitus (T2DM) is a polygenic disease that develops as a result of the interaction of hereditary predisposition and environmental factors. The predisposition to develop T2DM is associated with the inheritance of certain alleles of “healthy” genes. More than 100 polymorphic variants of genes that increase the risk of developing T2DM have already been described. Today, genes predisposing to the development of β-cell dysfunction and insulin resistance (IR) are the most well studied. In addition, genes that affect lipid metabolism and eating behavior and genes of some cytokines can participate in the formation of a genetic predisposition to the development of T2DM. Our article reviews the most promising potential areas of application of knowledge about the genetics of T2DM in clinical practice. The first direction is to specify the classification and stratification of T2DM into subclasses/clasters. The second one is an individual assessment of the risk of developing T2DM and its complications. Today, predictive models of the risk of developing type 2 diabetes are not accurate enough for widespread use in clinical practice, but now researchers are actively working to improve their accuracy and effectiveness. And finally, knowledge about the genetics of T2DM can help predict the effectiveness of glucose-lowering therapy. In this review, we also discuss the topic of metabolic disease endophenotypes. The concept of endophenotypes suggests the presence of certain pathogenic common links in the pathogenesis of IR, obesity, T2DM, cardiovascular diseases, non-alcoholic fatty liver disease and chronic kidney disease, which are based on certain polymorphic gene variants. The results of research in the field of genetics of T2DM give us new possibilities for a personalized approach to the management of this complex disease.

Abstract Background Though the heterogeneous expression of symptoms of borderline personality disorder (BPD) is well-known, it is far from fully understood. Hybrid models combining dimensional and categorical ways of diagnosing BPD have been suggested to better handle this heterogeneity, but more research is needed. The aim of this study was to identify potential clusters in BPD, and evaluate if these clusters differed in diagnostic composition, severity, psychiatric symptoms, emotion regulation and control, or sociodemographic features. Methods Clusters were based on personality traits measured with the Swedish universities Scales of Personality (SSP) in 141 psychiatric patients diagnosed with BPD. Hierarchical cluster analysis was performed using Ward’s method. We used one-way analysis of variance to explore the different clusters’ properties. Effect sizes were calculated using partial eta squared. Results We found three distinct clusters: the lower psychopathology cluster (N = 67), the externalizing cluster (N = 28), and the internalizing cluster (N = 46). The clusters differed regarding trait composition, severity, and emotion regulation and control. Conclusions Our findings support hybrid models for diagnosing BPD by showing that clusters differed in terms of both severity (lower and higher psychopathology) and personality traits/style (internalizing and externalizing). Assessment of personality traits may be a feasible way to differentiate between clusters. In the future, this knowledge might be used to personalize treatment.

Background: Bipolar disorder (BD) is a chronic multifactorial disorder that presents with cognitive impairment as one of its main features, in patients as well as in their first-degree relatives. However, the profile of cognitive dysfunction in BD patients and their relatives is not yet well defined. Various neurocognitive deficits have been proposed as endophenotypes for BD. In the present study, we explored the susceptibility to neurocognitive deficits in BD patients and their siblings compared to healthy controls. Method: A sample consisting of patients diagnosed with BD (N=37), their unaffected siblings (N=30) and a healthy control group (N=39) was assessed using the Brief Assessment of Cognition for Affective Disorders (BAC-A) battery of tests in various cognitive domains: memory, processing speed, working memory, reasoning and problem solving, and affective processing. Results: Compared to healthy controls, BD patients and their unaffected siblings showed deficits in attention and motor speed, or processing speed as measured by the Symbol coding task (p = 0.008), as well as a similar degree of impairment (p = 1.000). Limitations: The lack of statistically significant findings in the other cognitive domains could be related to differences in task difficulty. Most patients were taking psychotropic medication with varying effects on cognition and being treated as outpatients, implying a currently higher level of functioning, which may limit extrapolation of the sample to the general population of BD patients. Conclusions: These results support the view of considering processing speed as an endophenotype for bipolar disorder.

As some critics have stated, the term “developmental dyslexia” refers to a strictly human disorder, relating to a strictly human capacity – reading – so it cannot be modeled in experimental animals, much less so in lowly rodents. However, two endophenotypes associated with developmental dyslexia are eminently suitable for animal modeling: Cerebral Lateralization, as illustrated by the association between dyslexia and non-righthandedness, and Cerebrocortical Dysfunction, as illustrated by the described abnormal structural anatomy and/or physiology and functional imaging of the dyslexic cerebral cortex. This paper will provide a brief review of these two endophenotypes in human beings with developmental dyslexia and will describe the animal work done in my laboratory and that of others to try to shed light on the etiology of and neural mechanisms underlying developmental dyslexia. Some thought will also be given to future directions of the research.

Abstract Background Acute hypoxemic respiratory failure (HRF) is associated with high morbidity and mortality, but its heterogeneity challenges the identification of effective therapies. Defining subphenotypes with distinct prognoses or biologic features can improve therapeutic trials, but prior work has focused on ARDS, which excludes many acute HRF patients. We aimed to characterize persistent and resolving subphenotypes in the broader HRF population. Methods In this secondary analysis of 2 independent prospective ICU cohorts, we included adults with acute HRF, defined by invasive mechanical ventilation and PaO2-to-FIO2 ratio ≤ 300 on cohort enrollment (n = 768 in the discovery cohort and n = 1715 in the validation cohort). We classified patients as persistent HRF if still requiring mechanical ventilation with PaO2-to-FIO2 ratio ≤ 300 on day 3 following ICU admission, or resolving HRF if otherwise. We estimated relative risk of 28-day hospital mortality associated with persistent HRF, compared to resolving HRF, using generalized linear models. We also estimated fold difference in circulating biomarkers of inflammation and endothelial activation on cohort enrollment among persistent HRF compared to resolving HRF. Finally, we stratified our analyses by ARDS to understand whether this was driving differences between persistent and resolving HRF. Results Over 50% developed persistent HRF in both the discovery (n = 386) and validation (n = 1032) cohorts. Persistent HRF was associated with higher risk of death relative to resolving HRF in both the discovery (1.68-fold, 95% CI 1.11, 2.54) and validation cohorts (1.93-fold, 95% CI 1.50, 2.47), after adjustment for age, sex, chronic respiratory illness, and acute illness severity on enrollment (APACHE-III in discovery, APACHE-II in validation). Patients with persistent HRF displayed higher biomarkers of inflammation (interleukin-6, interleukin-8) and endothelial dysfunction (angiopoietin-2) than resolving HRF after adjustment. Only half of persistent HRF patients had ARDS, yet exhibited higher mortality and biomarkers than resolving HRF regardless of whether they qualified for ARDS. Conclusion Patients with persistent HRF are common and have higher mortality and elevated circulating markers of lung injury compared to resolving HRF, and yet only a subset are captured by ARDS definitions. Persistent HRF may represent a clinically important, inclusive target for future therapeutic trials in HRF.

Background: Identifying people at risk of developing bipolar disorder (BD) using endophenotypes is of recent interest. Few studies on social cognition in first-degree relatives of patients with BD have shown inconsistent findings. This study aimed to evaluate the social cognitive deficits (SCD) and its correlates among siblings of patients with BD. Methodology: In this cross-sectional study, patients of BD (n = 32), their siblings (n = 32), and healthy control (HC) subjects (n = 38) matched for age, gender, and education were evaluated on social cognition rating tools in Indian setting and neurocognitive tests (color trail test, Wisconsin card sorting test [WCST], and Hopkin's verbal learning test [HVLT]). Results: When the siblings of patients with BD were compared with patients with BD and the HCs, siblings (mean 0.6 [standard deviation [SD]: 0.2]) performed worse than the HCs (mean 0.9 [SD: 0.1]) (P ≤ 0.001) on the Faux pas composite index. Compared to HCs, siblings performed worse on all the subtests of HVLT (Trial delayed) (P < 0.001) and WCST (total correct, total errors, and conceptual responses) (P < 0.001). Conclusion: Presence of elevated level of SCD among siblings, especially the Faux pas composite index in comparison to HCs, imply that these are stable traits, which are more often present in the patients and at risk individuals. This implies that SCD can be considered as another important endophenotype for BD.

Objectives: Bipolar II depression (BD-II) is a subtype of bipolar disorder with recurrent depressive, manic, and frequent depressive episodes as the main clinical manifestations. This study aimed to compare the cognitive function of patients with BD-II with those of healthy siblings and controls to explore the internal phenotype of BD-II in the field of cognitive function. Methods: 66 BD-II patients, 58 healthy siblings, and 55 healthy controls were assessed with the Trail Making Test (TMT), Digit Symbol Coding Test (DSCT), Category Fluency, Hopkins Verbal Learning Test-Revised (HVLTR), Brief Visuospatial Memory Test-Revised (BVMT-R), Wechsler Memory Scale 3rd ed. Spatial Span Subtest (WMS-III SS), Neuropsychological Assessment Battery Mazes (NABM), Continuous Performance Test, and Identical Pairs (CPT-IP). Results: Patients with BD-II showed cognitive deficits in visual learning, reasoning and problem solving, verbal learning, attention/vigilance, working memory, and speed of processing. Healthy siblings showed cognitive deficits in reasoning and problem solving, verbal learning, attention/vigilance, working memory, and speed of processing. Substantial differences were observed among the three groups in reasoning and problem solving. Conclusions: Verbal learning, working memory, and attention/vigilance may be potential endophenotypes that can be used to identify BD-II among Han Chinese in the early stage.

Eating disorders (EDs) are psychiatric disorders with a neurobiological basis. ED-specific neuropsychological and brain characteristics have been identified, but often in individuals in the acute phase or recovered from EDs, precluding an understanding of whether they are correlates and scars of EDs vs. predisposing factors. Although familial high-risk (FHR) studies are available across other disorders, this study design has not been used in EDs. We carried out the first FMH study in EDs, investigating healthy offspring of women with EDs and controls. We preliminarily aimed to investigate ED-related neurocognitive and brain markers that could point to predisposing factors for ED. Sixteen girls at FHR for EDs and twenty control girls (age range: 8–15), completed neuropsychological tests assessing executive functions. Girls also underwent a resting-state fMRI scan to quantify functional connectivity (FC) within resting-state networks. Girls at FHR for EDs performed worse on a cognitive flexibility task compared with controls (F = 5.53, p = 0.02). Moreover, they showed different FC compared with controls in several resting-state networks (p < 0.05 FDR-corrected). Differences identified in cognitive flexibility and in FC are in line with those identified in individuals with EDs, strongly pointing to a role as potential endophenotypes of EDs.

An attention disturbance is a problem that affects many school-aged children. The assessment in children is usually report-based, and as a result, controversy surrounds the diagnosis. To solve this issue, the aim of this study was to develop a new tool to detect possible attention-related problems and impulsive behavior in 4- and 5-year-old children. This tool was developed as an Android app and could be used to provide an early indicator of possible future development problems. A sample of 103 children (48 girls and 55 boys) was randomly selected from primary schools and assessed by Pinky-Piggy, a videogame application based on a classical paradigm in experimental psychology. Data from this app were compared with a Child Neuropsychological Maturity Questionnaire. The subjects displayed different patterns of response to play a very simple game called Pinky-Piggy. The application discriminated between high-responders and low responders. The results showed a relationship between these two profiles and the levels of attention and neurodevelopment in each group. The tool could identify different types of profiles and demonstrated its potential to evaluate endophenotypes to predict attentional problems related to impulsive behavior. Additionally, it required less time and fewer tests to identify possible at-risk populations, thus assisting in clinical diagnosis.

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.

Abstract Studies of eye movement have become an essential tool of basic neuroscience research. Measures of eye movement have been applied to higher brain functions such as cognition, social behavior, and higher‐level decision‐making. With the development of eye trackers, a growing body of research has described eye movements in relation to mental disorders, reporting that the basic oculomotor properties of patients with mental disorders differ from those of healthy controls. Using discrimination analysis, several independent research groups have used eye movements to differentiate patients with schizophrenia from a mixed population of patients and controls. Recently, in addition to traditional oculomotor measures, several new techniques have been applied to measure and analyze eye movement data. One research group investigated eye movements in relation to the risk of autism spectrum disorder several years prior to the emergence of verbal‐behavioral abnormalities. Research on eye movement in humans in social communication is therefore considered important, but has not been well explored. Since eye movement patterns vary between patients with mental disorders and healthy controls, it is necessary to collect a large amount of eye movement data from various populations and age groups. The application of eye trackers in the clinical setting could contribute to the early treatment of mental disorders.

Shi-Ze Lin,1,2 Yan-Kun Wu,2–4 Yun-Ai Su,2–4 Tian-Mei Si2–41Quanzhou Mental Health Center, The Third Hospital of Quanzhou, Quanzhou, Fujian Province, People’s Republic of China; 2Institute of Mental Health, Peking University Sixth Hospital, Beijing, People’s Republic of China; 3National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, People’s Republic of China; 4NHC Key Laboratory of Mental Health, Peking University, Beijing, People’s Republic of ChinaAbstract: Prospective memory (PM) could be impaired in the non-psychotic first-degree relatives of patients with schizophrenia. This meta-analysis systematically examined the PM of non-psychotic first-degree relatives of patients with schizophrenia. Both Chinese and English databases were systematically searched for articles from the inception of the databases through November 13, 2018. Case-control studies of PM in non-psychotic first-degree relatives of patients with schizophrenia were included in the analyses. Confidence intervals (CIs) and standardized mean differences (SMDs) were calculated utilizing the random effects model. Four studies (n=268) that compared PM performance between non-psychotic first-degree relatives of patients with schizophrenia (n=136) and healthy controls (n=132) were included. Three studies were rated as “high quality”, while the quality of evidence of the three outcomes included in this meta-analysis was moderate. Compared with the healthy controls, the non-psychotic first-degree relatives of patients with schizophrenia showed impairments in overall PM (two studies, n=127; SMD: −0.46; 95% CI=−0.82, −0.11, P=0.01; I=0%), event-based PM (EBPM) (four studies, n=268; SMD: −0.56; 95% CI=−0.80, −0.31, P

Focal dystonias are the most common forms of isolated dystonia; however, the etiopathophysiological signatures of disorder penetrance and clinical manifestation remain unclear. Using an imaging genetics approach, we investigated functional and structural representations of neural endophenotypes underlying the penetrance and manifestation of laryngeal dystonia in families, including 21 probands and 21 unaffected relatives, compared to 32 unrelated healthy controls. We further used a supervised machine-learning algorithm to predict the risk for dystonia development in susceptible individuals based on neural features of identified endophenotypes. We found that abnormalities in prefrontal-parietal cortex, thalamus, and caudate nucleus were commonly shared between patients and their unaffected relatives, representing an intermediate endophenotype of laryngeal dystonia. Machine learning classified 95.2% of unaffected relatives as patients rather than healthy controls, substantiating that these neural alterations represent the endophenotypic marker of dystonia penetrance, independent of its symptomatology. Additional abnormalities in premotor-parietal-temporal cortical regions, caudate nucleus, and cerebellum were present only in patients but not their unaffected relatives, likely representing a secondary endophenotype of dystonia manifestation. Based on alterations in the parietal cortex and caudate nucleus, the machine learning categorized 28.6% of unaffected relative as patients, indicating their increased lifetime risk for developing clinical manifestation of dystonia. The identified endophenotypic neural markers may be implemented for screening of at-risk individuals for dystonia development, selection of families for genetic studies of novel variants based on their risk for disease penetrance, or stratification of patients who would respond differently to a particular treatment in clinical trials.

Schizophrenia (SCZ) is a severe psychotic disorder that affects up to 1% of the US population and it is associated with progressive impairment in social functioning and cognition. Nonetheless, despite its high burden, the pathophysiology of SCZ, including the genetic and biological mechanisms underlying the development and manifestation of the disorder, remains largely elusive. Endophenotypes are subtypes of biological markers that are more closely related to the genetic vulnerability for a disorder (e.g., SCZ). Recently, research on endophenotypes has identified several parameters that may prove useful in shedding light over the underlying neurobiology of SCZ. In this article, we provide an overview of the most established SCZ endophenotypes in the domains of neurocognition (attention deficits, working and verbal declarative memory dysfunctions) and neurophysiology (pre-pulse inhibition, anti-saccade impairment, event-related potential deficits) along with some novel, sleep-based measures (reduced sleep spindles and sleep slow waves). We also discuss recent conceptual advances in the field that may lead to novel, personalized treatment interventions for patients affected by this devastating mental illness.

Deficits in the gating of sensory stimuli, i.e., the ability to suppress the processing of irrelevant sensory input, are considered to play an important role in the pathogenesis of several neuropsychiatric disorders, in particular schizophrenia. Gating is disrupted both in schizophrenia patients and their unaffected relatives, suggesting that gating deficit may represent a biomarker associated with a genetic liability to the disorder. To assess the strength of the evidence for the etiopathogenetic links between genetic variation, gating efficiency, and schizophrenia, we carried out a systematic review of human genetic association studies of sensory gating (suppression of the P50 component of the auditory event-related brain potential) and sensorimotor gating (prepulse inhibition of the acoustic startle response). Sixty-three full-text articles met the eligibility criteria for inclusion in the review. In total, 117 genetic variants were reported to be associated with gating functions: 33 variants for sensory gating, 80 variants for sensorimotor gating, and four variants for both sensory and sensorimotor gating. However, only five of these associations (four for prepulse inhibition—CHRNA3 rs1317286, COMT rs4680, HTR2A rs6311, and TCF4 rs9960767, and one for P50 suppression—CHRNA7 rs67158670) were consistently replicated in independent samples. Although these variants and genes were all implicated in schizophrenia in research studies, only two polymorphisms (HTR2A rs6311 and TCF4 rs9960767) were also reported to be associated with schizophrenia at a meta-analytic or genome-wide level of evidence. Thus, although gating is widely considered as an important endophenotype of schizophrenia, these findings demonstrate that evidence for a common genetic etiology of impaired gating functions and schizophrenia is yet unsatisfactory, warranting further studies in this field.

Addiction management is complex, and it requires a bio-psycho-social perspective, that ought to consider the multiple etiological and developmental factors. Because of this, a large amount of resources has been allocated to assess the vulnerability to dependence, i.e., to identify the processes underlying the transition from substance use to dependence, as well as its course, in order to determine the key points in its prevention, treatment, and recovery. Consequently, knowledge \from neuroscience must be taken into account, which is why different initiatives have emerged with this objective, such as the “Research Domain Criteria” (RDoC), and the “Addiction Neuroclinical Assessment” (ANA). Particularly, neuropsychophysiological measures could be used as markers of cognitive and behavioral attributes or traits in alcohol dependence, and even trace clinical change. In this way, the aim of this narrative review is to provide an overview following ANA clinical framework, to the most robust findings in neuropsychophysiological changes in alcohol dependence, that underlie the main cognitive domains implicated in addiction: incentive salience, negative emotionality, and executive functioning. The most consistent results have been found in event-related potential (ERP) analysis, especially in the P3 component, that could show a wide clinical utility, mainly for the executive functions. The review also shows the usefulness of other components, implicated in affective and substance-related processing (P1, N1, or the late positive potential LPP), as well as event-related oscillations, such as theta power, with a possible use as vulnerability or clinical marker in alcohol dependence. Finally, new tools emerging from psychophysiology research, based on functional connectivity or brain graph analysis could help toward a better understanding of altered circuits in alcohol dependence, as well as communication efficiency and effort during mental operations. This review concludes with an examination of these tools as possible markers in the clinical field and discusses methodological differences, the need for more replicability studies and incipient lines of work. It also uses consistent findings in psychophysiology to draw possible treatment targets and cognitive profiles in alcohol dependence.

Background: Social anxiety disorder (SAD) is a mental illness with a complex, partially genetic background. Differences in characteristics of white matter (WM) microstructure have been reported in patients with SAD compared to healthy controls. Also, WM characteristics are moderately to highly heritable. Endophenotypes are measurable characteristics on the road from genotype to phenotype, putatively reflective of genetically based disease mechanisms. In search of candidate endophenotypes of SAD we used a unique sample of SAD patients and their family members of two generations to explore microstructure of WM tracts as candidate endophenotypes. We focused on two endophenotype criteria: co-segregation with social anxiety within the families, and heritability. Methods: Participants (n = 94 from 8 families genetically vulnerable for SAD) took part in the Leiden Family Lab Study on Social Anxiety Disorder (LFLSAD). We employed tract-based spatial statistics to examine structural WM characteristics, being fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD) and radial diffusivity (RD), in three a-priori defined tracts of interest: uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and inferior longitudinal fasciculus (ILF). Associations with social anxiety symptoms and heritability were estimated. Results: Increased FA in the left and right SLF co-segregated with symptoms of social anxiety. These findings were coupled with decreased RD and MD. All characteristics of WM microstructure were estimated to be at least moderately heritable. Conclusion: These findings suggest that alterations in WM microstructure in the SLF could be candidate endophenotypes of SAD, as they co-segregated within families genetically vulnerable for SAD and are heritable. These findings further elucidate the genetic susceptibility to SAD and improve our understanding of the overall etiology.

Social anxiety disorder (SAD) runs in families, but the neurobiological pathways underlying the genetic susceptibility towards SAD are largely unknown. Here, we employed an endophenotype approach, and tested the hypothesis that amygdala hyperreactivity to faces conditioned with a social-evaluative meaning is a candidate SAD endophenotype. We used data from the multiplex, multigenerational Leiden Family Lab study on Social Anxiety Disorder (eight families, n = 105) and investigated amygdala activation during a social-evaluative conditioning paradigm with high ecological validity in the context of SAD. Three neutral faces were repeatedly presented in combination with socially negative, positive or neutral sentences. We focused on two endophenotype criteria: co-segregation of the candidate endophenotype with the disorder within families, and heritability. Analyses of the fMRI data were restricted to the amygdala as a region of interest, and association analyses revealed that bilateral amygdala hyperreactivity in response to the conditioned faces co-segregated with social anxiety (SA; continuous measure) within the families; we found, however, no relationship between SA and brain activation in response to more specific fMRI contrasts. Furthermore, brain activation in a small subset of voxels within these amygdala clusters was at least moderately heritable. Taken together, these findings show that amygdala engagement in response to conditioned faces with a social-evaluative meaning qualifies as a neurobiological candidate endophenotype of social anxiety. Thereby, these data shed light on the genetic vulnerability to develop SAD.

Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach ”phenomic selection” (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.

The review summarizes the main results of studies on the influence of genetic factors on neurophysiological changes in neurodegenerative age-related diseases Alzheimer's (AD), Parkinson's (PD) and Huntington (HD) diseases. In some cases, neurophysiological methods make it possible to detect early changes already at the preclinical stage of neurodegenerative process. Such neurophysiological markers may be considered as endophenotypes and used for the early diagnosis of the diseases. The conducted studies are promising for clarifying which factors underlie the heterogeneity of diseases not only at the genetic level, but also at the neurophysiological level. At the same time, such an approach showed the presence of a number of neurophysiological alterations common to AD, PD, and HD. Disconnection of neural circuits, including interhemispheric disintegration, slowdown of information processes, disinhibition, hyperexcitability and epileptogenesis, as well as alterations in neurovascular coupling, are of great importance for the development of diseases. On the other hand, neurophysiological changes can directly affect the development of the disease, including the genetic level, as evidenced by experimental optogenetic studies, the results of deep brain stimulation and other neuromodulation methods. These data are valuable for a personalized approach to the prevention and treatment of age-dependent neurodegenerative diseases.

The characterization of biomarkers has been a central goal of research in psychiatry over the last years. While most of this research has focused on the identification of biomarkers, using various experimental approaches, it has been recognized that their instantiations, through computational models, have great potential to help us understand and interpret these experimental results. However, the enormous increase in available neurophysiological and neurocognitive as well as computational data also poses new challenges. How can a researcher stay on top of the experimental literature? How can computational modeling data be efficiently compared to experimental data? How can computational modeling most effectively inform experimentalists? Recently, a general scientific framework for the generation of executable tests that automatically compare model results to experimental observations, SciUnit, has been proposed. Here we exploit this framework for research in psychiatry to address the challenges mentioned. We extend the SciUnit framework by adding an experimental database, which contains a comprehensive collection of relevant experimental observations, and a prediction database, which contains a collection of predictions generated by computational models. Together with appropriately designed SciUnit tests and methods to mine and visualize the databases, model data, and test results, this extended framework has the potential to greatly facilitate the use of computational models in psychiatry. As an initial example, we present ASSRUnit, a module for auditory steady-state response deficits in psychiatric disorders.

The characterization of biomarkers has been a central goal of research in psychiatry over the last years. While most of this research has focused on the identification of biomarkers, using various experimental approaches, it has been recognized that their instantiations, through computational models, have great potential to help us understand and interpret these experimental results. However, the enormous increase in available neurophysiological and neurocognitive as well as computational data also poses new challenges. How can a researcher stay on top of the experimental literature? How can computational modeling data be efficiently compared to experimental data? How can computational modeling most effectively inform experimentalists? Recently, a general scientific framework for the generation of executable tests that automatically compare model results to experimental observations, SciUnit, has been proposed. Here we exploit this framework for research in psychiatry to address the challenges mentioned. We extend the SciUnit framework by adding an experimental database, which contains a comprehensive collection of relevant experimental observations, and a prediction database, which contains a collection of predictions generated by computational models. Together with appropriately designed SciUnit tests and methods to mine and visualize the databases, model data, and test results, this extended framework has the potential to greatly facilitate the use of computational models in psychiatry. As an initial example, we present ASSRUnit, a module for auditory steady-state response deficits in psychiatric disorders.

Abstract Background There is emerging evidence that there are shared genetic, environmental and developmental risk factors in psychiatry, that cut across traditional diagnostic boundaries. With this background, the Discovery biology of neuropsychiatric syndromes (DBNS) proposes to recruit patients from five different syndromes (schizophrenia, bipolar disorder, obsessive compulsive disorder, Alzheimer’s dementia and substance use disorders), identify those with multiple affected relatives, and invite these families to participate in this study. The families will be assessed: 1) To compare neuro-endophenotype measures between patients, first degree relatives (FDR) and healthy controls., 2) To identify cellular phenotypes which differentiate the groups., 3) To examine the longitudinal course of neuro-endophenotype measures., 4) To identify measures which correlate with outcome, and 5) To create a unified digital database and biorepository. Methods The identification of the index participants will occur at well-established specialty clinics. The selected individuals will have a strong family history (with at least another affected FDR) of mental illness. We will also recruit healthy controls without family history of such illness. All recruited individuals (N = 4500) will undergo brief clinical assessments and a blood sample will be drawn for isolation of DNA and peripheral blood mononuclear cells (PBMCs). From among this set, a subset of 1500 individuals (300 families and 300 controls) will be assessed on several additional assessments [detailed clinical assessments, endophenotype measures (neuroimaging- structural and functional, neuropsychology, psychophysics-electroencephalography, functional near infrared spectroscopy, eye movement tracking)], with the intention of conducting repeated measurements every alternate year. PBMCs from this set will be used to generate lymphoblastoid cell lines, and a subset of these would be converted to induced pluripotent stem cell lines and also undergo whole exome sequencing. Discussion We hope to identify unique and overlapping brain endophenotypes for major psychiatric syndromes. In a proportion of subjects, we expect these neuro-endophenotypes to progress over time and to predict treatment outcome. Similarly, cellular assays could differentiate cell lines derived from such groups. The repository of biomaterials as well as digital datasets of clinical parameters, will serve as a valuable resource for the broader scientific community who wish to address research questions in the area.

Abstract Background Given the complex and progressive nature of Alzheimer’s disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. Methods Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer’s Disease Assessment Scale—cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups’ distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. Results The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. Conclusions Our results, ‘learned’ from clinical data, indicate the existence of at least three subgroups of Alzheimer’s patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.

Temporal processing (TP) is associated with functions such as perception, verbal skills, temporal perspective, and future planning, and is intercorrelated with working memory, attention, and inhibitory control, which are highly impaired in individuals with attention deficit hyperactivity disorder (ADHD). Here we evaluate TP measures as potential endophenotypes in Caribbean families ascertained from probands affected by ADHD. A total of 232 individuals were recruited and clinically evaluated using an extensive battery of neuropsychological tasks and reaction time (RT)-based task paradigms. Further, the heritability (genetic variance underpinning phenotype) was estimated as a measure of the genetics apportionment. A predictive framework for ADHD diagnosis was derived using these tasks. We found that individuals with ADHD differed from controls in neuropsychological tasks assessing mental control, visual-verbal memory, verbal fluency, verbal, and semantic fluency. In addition, TP measures such as RT, errors, and variability were also affected in individuals with ADHD. Moreover, we determined that only omission and commission errors had significant heritability. In conclusion, we have disentangled omission and commission errors as possible TP endophenotypes in ADHD, which can be suitable to assess the neurobiological and genetic basis of ADHD. A predictive model using these endophenotypes led to remarkable sensitivity, specificity, precision and classification rate for ADHD diagnosis, and may be a useful tool for patients’ diagnosis, follow-up, and longitudinal assessment in the clinical setting.

This article is the first of four articles designed to explore the complex interrelationship between Autism Spectrum Disorders (ASD); Obsessive compulsive and Related Disorders (OCRD) and Tic Disorders/Tourette’s Syndrome (TD/TS). We begin with an overview TD/TS and follow-up with reviews of OCRD and ASD. The final article in this series represents a synthesis of the neurobiological and genetic markers shared by patients presenting with all three syndromes. The goal is to describe the complex endophenotype of these patients in an effort to better define gene markers that underlie these heterogeneous clinical syndromes. Tic disorders (TD) are a collection of hyperkinetic movements that begin in early childhood. Tics are transient for most affected preschool children but a subgroup development persistent movements or progress to develop Tourette Syndrome (TS). TDs as a group display high heritability rates but definitive gene markers still elude us. The difficulty defining genetic markers is in large part due to the diverse neurodevelopmental trajectory, changing topography and typology, development of a broad spectrum of neurocognitive and behavioral complications, and a mixed pattern of psychiatric comorbidities.

Attention deficit hyperactivity disorder (ADHD) is a highly heritable neurobehavioral disorder that affects children worldwide, with detrimental long-term consequences in affected individuals. ADHD-affected patients display visual–motor and visuospatial abilities and skills that depart from those exhibited by non-affected individuals and struggle with perceptual organization, which might partially explain impulsive responses. Endophenotypes (quantifiable or dimensional constructs that are closely related to the root cause of the disease) might provide a more powerful and objective framework for dissecting the underlying neurobiology of ADHD than that of categories offered by the syndromic classification. In here, we explore the potential presence of the linkage and association of single-nucleotide polymorphisms (SNPs), harbored in genes implicated in the etiology of ADHD (ADGRL3, DRD4, and FGF1), with cognitive endophenotypes related to working memory and perceptual organization in 113 nuclear families. These families were ascertained from a geographical area of the Caribbean coast, in the north of Colombia, where the community is characterized by its ethnic diversity and differential gene pool. We found a significant association and linkage of markers ADGRL3-rs1565902, DRD4-rs916457 and FGF1-rs2282794 to neuropsychological tasks outlining working memory and perceptual organization such as performance in the digits forward and backward, arithmetic, similarities, the completion of figures and the assembly of objects. Our results provide strong support to understand ADHD as a combination of working memory and perceptual organization deficits and highlight the importance of the genetic background shaping the neurobiology, clinical complexity, and physiopathology of ADHD. Further, this study supplements new information regarding an ethnically diverse community with a vast African American contribution, where ADHD studies are scarce.

Despite extensive research, there is no convincing evidence of a reliable diagnostic biomarker for schizophrenia beyond clinical observation. Disorders of glutamatergic neurotransmission associated with N-methyl-D-aspartate (NMDA) receptor insufficiency, neuroinflammation, and redox dysregulation are the principal common mechanism linking changes in the periphery with the brain, ultimately contributing to the emergence of negative symptoms of schizophrenia that underlie differential diagnosis. The aim of the study was to evaluate the influence of these systems via peripheral and cerebral biochemical indices in relation to the patient’s clinical condition. Using neuroimaging diagnostics, we were able to define endophenotypes of schizophrenia based on objective laboratory data that form the basis of a personalized approach to diagnosis and treatment. The two distinguished endophenotypes differed in terms of the quality of life, specific schizophrenia symptoms, and glutamatergic neurotransmission metabolites in the anterior cingulate gyrus. Our results, as well as further studies of the excitatory or inhibitory balance of microcircuits, relating the redox systems on the periphery with the distant regions of the brain might allow for predicting potential biomarkers of neuropsychiatric diseases, including schizophrenia. To the best of our knowledge, our study is the first to identify an objective molecular biomarker of schizophrenia outcome.

Learning disabilities (LDs) have an estimated prevalence between 5% and 9% in the pediatric population and are associated with difficulties in reading, arithmetic, and writing. Previous electroencephalography (EEG) research has reported a lag in alpha-band development in specific LD phenotypes, which seems to offer a possible explanation for differences in EEG maturation. In this study, 40 adolescents aged 10–15 years with LDs underwent 10 sessions of Live Z-Score Training Neurofeedback (LZT-NF) Training to improve their cognition and behavior. Based on the individual alpha peak frequency (i-APF) values from the spectrogram, a group with normal i-APF (ni-APF) and a group with low i-APF (li-APF) were compared in a pre-and-post-LZT-NF intervention. There were no statistical differences in age, gender, or the distribution of LDs between the groups. The li-APF group showed a higher theta absolute power in P4 (p = 0.016) at baseline and higher Hi-Beta absolute power in F3 (p = 0.007) post-treatment compared with the ni-APF group. In both groups, extreme waves (absolute Z-score of ≥1.5) were more likely to move toward the normative values, with better results in the ni-APF group. Conversely, the waves within the normal range at baseline were more likely to move out of the range after treatment in the li-APF group. Our results provide evidence of a viable biomarker for identifying optimal responders for the LZT-NF technique based on the i-APF metric reflecting the patient’s neurophysiological individuality.

Over the last decade, the field of imaging genomics has combined high-throughput genotype data with quantitative magnetic resonance imaging (QMRI) measures to identify genes associated with brain structure, cognition, and several brain-related disorders. Despite its successful application in different psychiatric and neurological disorders, the field has yet to be advanced in epilepsy. In this article we examine the relevance of imaging genomics for future genetic studies in epilepsy from three perspectives. First, we discuss prior genome-wide genetic mapping efforts in epilepsy, considering the possibility that some studies may have been constrained by inherent theoretical and methodological limitations of the genome-wide association study (GWAS) method. Second, we offer a brief overview of the imaging genomics paradigm, from its original inception, to its role in the discovery of important risk genes in a number of brain-related disorders, and its successful application in large-scale multinational research networks. Third, we provide a comprehensive review of past studies that have explored the eligibility of brain QMRI traits as endophenotypes for epilepsy. While the breadth of studies exploring QMRI-derived endophenotypes in epilepsy remains narrow, robust syndrome-specific neuroanatomical QMRI traits have the potential to serve as accessible and relevant intermediate phenotypes for future genetic mapping efforts in epilepsy.

Although substantial heritability has been reported and candidate genes have been identified, we are far from understanding the etiopathogenetic pathways underlying developmental dyslexia (DD). Reading-related endophenotypes (EPs) have been established. Until now it was unknown whether they mediated the pathway from gene to reading (dis)ability. Thus, in a sample of 223 siblings from nuclear families with DD and 79 unrelated typical readers, we tested four EPs (i.e., rapid auditory processing, rapid automatized naming, multisensory nonspatial attention and visual motion processing) and 20 markers spanning five DD-candidate genes (i.e., DYX1C1, DCDC2, KIAA0319, ROBO1 and GRIN2B) using a multiple-predictor/multiple-mediator framework. Our results show that rapid auditory and visual motion processing are mediators in the pathway from ROBO1-rs9853895 to reading. Specifically, the T/T genotype group predicts impairments in rapid auditory and visual motion processing which, in turn, predict poorer reading skills. Our results suggest that ROBO1 is related to reading via multisensory temporal processing. These findings support the use of EPs as an effective approach to disentangling the complex pathways between candidate genes and behavior.

Objective: Bipolar disorder (BD) is highly heritable. The present study aimed at identifying brain morphometric features that could represent markers of BD vulnerability in non-bipolar relatives of bipolar patients. Methods: In the present study, structural magnetic resonance imaging brain scans were acquired from a total of 93 subjects, including 31 patients with BD, 31 non-bipolar relatives of BD patients, and 31 healthy controls. Volumetric measurements of the anterior cingulate cortex (ACC), lateral ventricles, amygdala, and hippocampus were completed using the automated software FreeSurfer. Results: Analysis of covariance (with age, gender, and intracranial volume as covariates) indicated smaller left ACC volumes in unaffected relatives as compared to healthy controls and BD patients (p = 0.004 and p = 0.037, respectively). No additional statistically significant differences were detected for other brain structures. Conclusion: Our findings suggest smaller left ACC volume as a viable biomarker candidate for BD.

Investigation of objective and quantitative behavioral phenotypes along with neurobiological endophenotypes might lead to increased knowledge of the mechanisms that underlie autism spectrum disorders (ASD). Here, we investigated the association between locomotor dynamics and characteristics of the acoustic startle response (ASR) and its modulation in ASD (n = 14) and typically developing (TD, n = 13) children. The ASR was recorded in response to acoustic stimuli in increments of 10 dB (65–105 dB SPL). We calculated the average ASR magnitude for each stimulus intensity and peak-ASR latency. Locomotor activity was continuously measured with a watch-type actigraph. We examined statistics of locomotor activity, such as mean activity levels and the skewness of activity. Children with ASD had a significantly greater ASR magnitude in response to a weak acoustic stimulus, which reflects acoustic hyper-reactivity. The skewness of all-day activity was significantly more negative in children with ASD than those with TD. Skewness of daytime activity was also more negative, although only of borderline statistical significance. For all children, the higher mean and more negatively skewed daytime activity, reflecting hyperactivity that was associated with sporadic large daytime “troughs,” was significantly correlated with acoustic hyper-reactivity. The more negatively skewed locomotor activity occurring in the daytime was also associated with impaired sensorimotor gating, examined as prepulse inhibition at a prepulse intensity of 70 dB. This comprehensive investigation of locomotor dynamics and the ASR extends our understanding of the neurophysiology that underlies ASD.

Visual masking is a frequently used tool in schizophrenia research. Visual masking has a very high sensitivity and specificity and masking paradigms have been proven to be endophenotypes. Whereas masking is a powerful technique to study schizophrenia, the underlying mechanisms are discussed controversially. For example, for more than 25 years, masking deficits of schizophrenia patients were mainly attributed to a deficient magno-cellular system (M-system). Here, we show that there is very little evidence that masking deficits are magno-cellular deficits. We will discuss the magno-cellular and other approaches in detail and highlight their pros and cons.

Gene–environment interactions (GEIs) likely play significant roles in the pathogenesis of schizophrenia and underlie differences in pathological, behavioral, and clinical presentations of the disease. Findings from epidemiology and psychiatric genetics have assisted in the generation of animal models of GEI relevant to schizophrenia. These models may provide a foundation for elucidating the molecular, cellular, and circuitry mechanisms that mediate GEI in schizophrenia. Here we critically review current mouse models of GEI related to schizophrenia, describe directions for their improvement, and propose endophenotypes to provide a more tangible basis for molecular studies of pathways of GEI and facilitate the identification of novel therapeutic targets.

Objective: Unaffected relatives of bipolar disorder (BD) patients have been investigated for the identification of endophenotypes in an attempt to further elucidate the pathophysiology of the disease. Brain-derived neurotrophic factor (BDNF) is considered to be implicated in the pathophysiology of BD, but its role as an endophenotype has been poorly studied. We investigated abnormal serum BDNF levels in BD patients, in their unaffected relatives, and in healthy controls. Methods: BDNF levels were obtained from 25 DSM-IV bipolar I disorder patients, 23 unaffected relatives, and 27 healthy controls. All BD patients were in remission. The unaffected subjects were first-degree relatives of the proband who had no lifetime DSM-IV diagnosis of axis I disorder. BDNF serum levels were determined by sandwich ELISA using monoclonal BDNF-specific antibodies. Results: There were no statistical differences in BDNF levels among BD patients, relatives, and healthy controls. Conclusion: Serum BDNF levels may not indicate high genetic risk for BD, possibly acting as state markers rather than trait markers of the disease.

Reductions in dendritic arbor length and complexity are among the most consistently replicated changes in neuronal structure in post mortem studies of cerebral cortical samples from subjects with schizophrenia, however, the underlying molecular mechanisms have not been identified. This study is the first to identify an alteration in a regulatory protein which is known to promote both dendritic length and arborization in developing neurons, Kalirin-9. We found Kalirin-9 expression to be paradoxically increased in schizophrenia. We followed up this observation by overexpressing Kalirin-9 in mature primary neuronal cultures, causing reduced dendritic length and complexity. Kalirin-9 overexpression represents a potential mechanism for dendritic changes seen in schizophrenia.

Anorexia nervosa (AN) is a very debilitating disorder and has shown different cognitive deficit patterns. Some of them are controversial because they relate some deficit with autistic traits. Both, social cognition and executive function are top-down processes that regulate social interaction and adaptive behavior in a complex world. Neurocognitive profiles focused on this process are especially useful as endophenotypes in clinic research and intervention.The aims of this study are (1) assessing the contribution of social cognition and executive function to socio-emotional and neurocognitive patterns in anorexia nervosa and (2) investigating the possible relationships between social cognition tasks and executive function measures and clinic features in this eating disorder. Fifteen adolescents with anorexia nervosa and 15 healthy controls took part in this study. Patients were diagnosed with DSM-IV-R criteria. They had suffered the disease for 3 years (SD ± 1.47). They were compared in social cognition (Reading the Mind in the Eyes and the IOWA Gambling Task) and executive function (verbal fluency tasks, trail making test, and digit span test [DST] from the WAIS-III). The results show that there was a significant difference in social cognition, at the theory of mind and the IOWA gambling task. Finally, for the executive function task, adolescents with AN displayed significant differences in both direct DST and inverse DST, as well as in semantic fluency and phonological fluency compared with controls. These domains show different highly-significant relationships that could explain neurocognitive profiles. In conclusion, many differences were found between adolescents with AN and healthy controls. AN patients displayed important deficits in theory of mind recognition, executive function, and decision making. These deficits could play a role in maintaining pathological patterns and also important endophenotypes in research and intervention might be considered.

A B S T R A C T “Addiction” is a multifaceted complicated disorder with many interrelated causes, as well as environmental and genetic features. Several hereditary variables that have an effect on these features might work in together to influence vulnerability and the extent of being an addict. Molecular re-sequencing of the latest and formerly researched genes holds a crucial place with regards to the breakthrough of hereditary alternates of possible interest. This report presents a brief review of this complicated disorder through genotyping and phenotyping aspects, and examines their correlation in creating and driving this disease.

A B S T R A C T “Addiction” is a multifaceted complicated disorder with many interrelated causes, as well as environmental and genetic features. Several hereditary variables that have an effect on these features might work in together to influence vulnerability and the extent of being an addict. Molecular re-sequencing of the latest and formerly researched genes holds a crucial place with regards to the breakthrough of hereditary alternates of possible interest. This report presents a brief review of this complicated disorder through genotyping and phenotyping aspects, and examines their correlation in creating and driving this disease.

Many studies have examined either electroencephalogram (EEG) frequency activity or gray matter volumes (GMV) in various psychoses (including schizophrenia, schizoaffective and psychotic bipolar disorder). Prior work demonstrated similar EEG and gray matter abnormalities in both schizophrenia and psychotic bipolar disorder. Integrating EEG and GMV and jointly analyzing the combined data fully elucidates the linkage between the two and may provide better biomarker- or endophenotype-specificity for a particular illness. Joint exploratory investigations of EEG and GMV are scarce in the literature and the relationship between the two in psychosis is even less explored. We investigated a joint multivariate model to test whether the linear relationship or linkage between awake EEG (AEEG) frequency activity and GMV is abnormal across the psychosis dimension and if such effects are also present in first-degree relatives.We assessed 607 subjects comprising 264 probands (105 schizophrenia (SZ), 72 schizoaffective (SZA) and 87 psychotic bipolar disorder (PBP)), 233 of their first degree relatives (82 SZ relatives (SZR), 71 SZA relatives (SZAR) and 80 PBP relatives (PBPR)) and 110 healthy comparison subjects (HC). All subjects underwent structural MRI (sMRI) and EEG scans. Frequency activity and voxel based morphometric GMV were derived from EEG and sMRI data respectively. Seven AEEG frequency and gray matter components were extracted using Joint independent component analysis (jICA). The loading coefficients (LC) were examined for group differences using analysis of covariance. Further, the LCs were correlated with psychopathology scores to identify relationship with clinical symptoms. Joint ICA revealed a single component differentiating SZ from HC (p