scholarly journals Potential scalp stimulation targets for mental disorders: evidence from neuroimaging studies

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jin Cao ◽  
Thalia Celeste Chai-Zhang ◽  
Yiting Huang ◽  
Maya Nicole Eshel ◽  
Jian Kong
Keyword(s):  
Mental Disorders ◽  
Large Scale ◽  
Psychiatric Symptoms ◽  
Common Mental Disorders ◽  
Target Selection ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Transcranial Electrical Stimulation ◽  
Post Traumatic Stress ◽  
Compulsive Disorder

AbstractMental disorders widely contribute to the modern global disease burden, creating a significant need for improvement of treatments. Scalp stimulation methods (such as scalp acupuncture and transcranial electrical stimulation) have shown promising results in relieving psychiatric symptoms. However, neuroimaging findings haven’t been well-integrated into scalp stimulation treatments. Identifying surface brain regions associated with mental disorders would expand target selection and the potential for these interventions as treatments for mental disorders. In this study, we performed large-scale meta-analyses separately on eight common mental disorders: attention deficit hyperactivity disorder, anxiety disorder, autism spectrum disorder, bipolar disorder, compulsive disorder, major depression, post-traumatic stress disorder and schizophrenia; utilizing modern neuroimaging literature to summarize disorder-associated surface brain regions, and proposed neuroimaging-based target protocols. We found that the medial frontal gyrus, the supplementary motor area, and the dorsal lateral prefrontal cortex are commonly involved in the pathophysiology of mental disorders. The target protocols we proposed may provide new brain targets for scalp stimulation in the treatment of mental disorders, and facilitate its clinical application.

The structure of common DSM-IV and ICD-10 mental disorders in the Australian general population

2006 ◽  
Vol 36 (11) ◽  
pp. 1593-1600 ◽  
Author(s):  
TIM SLADE ◽  
DAVID WATSON
Keyword(s):  
Mental Disorders ◽  
Large Scale ◽  
Factor Model ◽  
Common Mental Disorders ◽  
Composite International Diagnostic Interview ◽  
Well Being ◽  
Post Traumatic Stress ◽  
Compulsive Disorder ◽  
Icd 10 ◽  
Dsm Iv

Background. Patterns of co-occurrence among the common mental disorders may provide information about underlying dimensions of psychopathology. The aim of the current study was to determine which of four models best fits the pattern of co-occurrence between 10 common DSM-IV and 11 common ICD-10 mental disorders.Method. Data were from the Australian National Survey of Mental Health and Well-Being (NSMHWB), a large-scale community epidemiological survey of mental disorders. Participants consisted of a random population-based sample of 10641 community volunteers, representing a response rate of 78%. DSM-IV and ICD-10 mental disorder diagnoses were obtained using the Composite International Diagnostic Interview (CIDI), version 2.0. Confirmatory factor analysis (CFA) was used to assess the relative fit of competing models.Results. A hierarchical three-factor variation of a two-factor model demonstrated the best fit to the correlations among the mental disorders. This model included a distress factor with high loadings on major depression, dysthymia, generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD) and neurasthenia (ICD-10 only); a fear factor with high loadings on social phobia, panic disorder, agoraphobia and obsessive–compulsive disorder (OCD); and an externalizing factor with high loadings on alcohol and drug dependence. The distress and fear factors were best conceptualized as subfactors of a higher order internalizing factor.Conclusions. A greater focus on underlying dimensions of distress, fear and externalization is warranted.

scholarly journals ENIGMA and Global Neuroscience: A Decade of Large-Scale Studies of the Brain in Health and Disease across more than 40 Countries

2019 ◽  
Author(s):  
Paul Thompson ◽  
Neda Jahanshad ◽  
Christopher R. K. Ching ◽  
Lauren Salminen ◽  
Sophia I Thomopoulos ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
22Q11.2 Deletion Syndrome ◽  
Autism Spectrum ◽  
Deletion Syndrome ◽  
Sex And Gender ◽  
Post Traumatic Stress ◽  
Global Alliance ◽  
Compulsive Disorder ◽  
Health And Disease

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1,400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive and psychosocial factors.

scholarly journals Development of the RMT20, a composite screener to identify common mental disorders

BJPsych Open ◽  
2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Philip J. Batterham ◽  
Matthew Sunderland ◽  
Natacha Carragher ◽  
Alison L. Calear
Keyword(s):  
Psychological Distress ◽  
Anxiety Disorder ◽  
Mental Disorders ◽  
Large Scale ◽  
Common Mental Disorders ◽  
Area Under The Curve ◽  
Post Traumatic Stress ◽  
Cross Sectional ◽  
Epidemiological Surveys ◽  
General Psychological Distress

Background There are few very brief measures that accurately identify multiple common mental disorders. Aims The aim of this study was to develop and assess the psychometric properties of a new composite measure to screen for five common mental disorders. Method Two cross-sectional psychometric surveys were used to develop (n = 3175) and validate (n = 3620) the new measure, the Rapid Measurement Toolkit-20 (RMT20) against diagnostic criteria. The RMT20 was tested against a DSM-5 clinical checklist for major depression, generalised anxiety disorder, panic disorder, social anxiety disorder and post-traumatic stress disorder, with comparison with two measures of general psychological distress: the Kessler-10 and Distress Questionnaire-5. Results The area under the curve for the RMT20 was significantly greater than for the distress measures, ranging from 0.86 to 0.92 across the five disorders. Sensitivity and specificity at prescribed cut-points were excellent, with sensitivity ranging from 0.85 to 0.93 and specificity ranging from 0.73 to 0.83 across the five disorders. Conclusions The RMT20 outperformed two established scales assessing general psychological distress, is free to use and has low respondent burden. The measure is well-suited to clinical screening, internet-based screening and large-scale epidemiological surveys.

scholarly journals Young pregnant women and risk for mental disorders: findings from an early pregnancy cohort

BJPsych Open ◽  
2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Georgia Lockwood Estrin ◽  
Elizabeth G. Ryan ◽  
Kylee Trevillion ◽  
Jill Demilew ◽  
Debra Bick ◽  
...  
Keyword(s):  
High Risk ◽  
Mental Disorders ◽  
Pregnant Women ◽  
Young Women ◽  
Common Mental Disorders ◽  
Adverse Outcomes ◽  
Post Traumatic Stress ◽  
Cross Sectional ◽  
Compulsive Disorder ◽  
Population Prevalence

BackgroundYoung women aged 16–24 are at high risk of common mental disorders (CMDs), but the risk during pregnancy is unclear.AimsTo compare the population prevalence of CMDs in pregnant women aged 16–24 with pregnant women ≥25 years in a representative cohort, hypothesising that younger women are at higher risk of CMDs (depression, anxiety disorders, post-traumatic stress disorder, obsessive–compulsive disorder), and that this is associated with low social support, higher rates of lifetime abuse and unemployment.MethodAnalysis of cross-sectional baseline data from a cohort of 545 women (of whom 57 were aged 16–24 years), attending a South London maternity service, with recruitment stratified by endorsement of questions on low mood, interviewed with the Structured Clinical Interview DSM-IV-TR.ResultsPopulation prevalence estimates of CMDs were 45.1% (95% CI 23.5–68.7) in young women and 15.5% (95% CI 12.0–19.8) in women ≥25, and for ‘any mental disorder’ 67.2% (95% CI 41.7–85.4) and 21.2% (95% CI 17.0–26.1), respectively. Young women had greater odds of having a CMD (adjusted odds ratio (aOR) = 5.8, 95% CI 1.8–18.6) and CMDs were associated with living alone (aOR = 3.0, 95% CI 1.1–8.0) and abuse (aOR = 1.5, 95% CI 0.8–2.8).ConclusionsPregnant women between 16 and 24 years are at very high risk of mental disorders; services need to target resources for pregnant women under 25, including those in their early 20s. Interventions enhancing social networks, addressing abuse and providing adequate mental health treatment may minimise adverse outcomes for young women and their children.Declaration of interestNone.

scholarly journals Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders

2021 ◽  
Author(s):  
Nevena V. Radonjić ◽  
Jonathan L. Hess ◽  
Paula Rovira ◽  
Ole Andreassen ◽  
Jan K. Buitelaar ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Correlations ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Genetic Etiology ◽  
Compulsive Disorder ◽  
Pairwise Correlations ◽  
Genomewide Association ◽  
Structural Brain

AbstractGenomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. From the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. The SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD. BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.494). Our results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures.

scholarly journals Perinatal mental health around the world: priorities for research and service development in Africa

2020 ◽  
Vol 17 (3) ◽  
pp. 56-59 ◽  
Author(s):  
Mwawi Ng'oma ◽  
Tesera Bitew ◽  
Malinda Kaiyo-Utete ◽  
Charlotte Hanlon ◽  
Simone Honikman ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Disorders ◽  
Partner Violence ◽  
Large Scale ◽  
Infant Health ◽  
Common Mental Disorders ◽  
Maternal Mental Health ◽  
Service Development ◽  
Middle Income ◽  
The Right

Africa is a diverse and changing continent with a rapidly growing population, and the mental health of mothers is a key health priority. Recent studies have shown that: perinatal common mental disorders (depression and anxiety) are at least as prevalent in Africa as in high-income and other low- and middle-income regions; key risk factors include intimate partner violence, food insecurity and physical illness; and poor maternal mental health is associated with impairment of infant health and development. Psychological interventions can be integrated into routine maternal and child healthcare in the African context, although the optimal model and intensity of intervention remain unclear and are likely to vary across settings. Future priorities include: extension of research to include neglected psychiatric conditions; large-scale mixed-method studies of the causes and consequences of perinatal common mental disorders; scaling up of locally appropriate evidence-based interventions, including prevention; and advocacy for the right of all women in Africa to safe holistic maternity care.

The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review (Preprint)

2021 ◽  
Author(s):  
Alaa Abd-Alrazaq ◽  
Jens Schneider ◽  
Dari Alhuwail ◽  
Carla T Toro ◽  
Arfan Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mental Disorders ◽  
Systematic Reviews ◽  
Autism Spectrum ◽  
Great Promise ◽  
Routine Practice ◽  
Daily Routine ◽  
Reference List ◽  
Umbrella Review ◽  
Compulsive Disorder

BACKGROUND Diagnosing mental disorders is usually not an easy task and requires a large amount of time and effort given the complex nature of mental disorders. Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. OBJECTIVE This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. METHODS To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. Specifically, results of the included reviews were grouped based on the target mental disorders that the AI classifiers distinguish. RESULTS We included 15 systematic reviews of 852 citations identified by searching all databases. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n=7), mild cognitive impairment (n=6), schizophrenia (n=3), bipolar disease (n=2), autism spectrum disorder (n=1), obsessive-compulsive disorder (n=1), post-traumatic stress disorder (n=1), and psychotic disorders (n=1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. CONCLUSIONS AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. To expedite progress towards these technologies being incorporated into routine practice, we recommend that healthcare professionals in the field cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety. CLINICALTRIAL CRD42021231558

scholarly journals The Impact of MNRI Therapy on the Levels of Neurotransmitters Associated with Inflammatory Processes

2020 ◽  
Vol 21 (4) ◽  
pp. 1358 ◽  
Author(s):  
Tatiana V. Tatarinova ◽  
Trina Deiss ◽  
Lorri Franckle ◽  
Susan Beaven ◽  
Jeffrey Davis
Keyword(s):  
Attention Deficit ◽  
Attention Deficit Disorder ◽  
Emotional Regulation ◽  
Cognitive Skills ◽  
Developmental Disorders ◽  
Negative Impact ◽  
Autism Spectrum ◽  
Post Traumatic Stress ◽  
Compulsive Disorder ◽  
The Impact

The neurotransmitter levels of representatives from five different diagnosis groups were tested before and after participation in the MNRI®—Masgutova Neurosensorimotor Reflex Intervention. The purpose of this study was to ascertain neurological impact on (1) Developmental disorders, (2) Anxiety disorders/OCD (Obsessive Compulsive Disorder), PTSD (Post-Traumatic Stress disorder), (3) Palsy/Seizure disorders, (4) ADD/ADHD (Attention Deficit Disorder/Attention Deficit Disorder Hyperactive Disorder), and (5) ASD (Autism Spectrum Disorder) disorders. Each participant had a form of neurological dysregulation and typical symptoms respective to their diagnosis. These diagnoses have a severe negative impact on the quality of life, immunity, stress coping, cognitive skills, and social assimilation. This study showed a trend towards optimization and normalization of neurological and immunological functioning, thus supporting the claim that the MNRI method is an effective non-pharmacological neuromodulation treatment of neurological disorders. The effects of MNRI on inflammation have not yet been assessed. The resulting post-MNRI changes in participants’ neurotransmitters show significant adjustments in the regulation of the neurotransmitter resulting in being calmer, a decrease of hypervigilance, an increase in stress resilience, behavioral and emotional regulation improvements, a more positive emotional state, and greater control of cognitive processes. In this paper, we demonstrate that the MNRI approach is an intervention that reduces inflammation. It is also likely to reduce oxidative stress and encourage homeostasis of excitatory neurotransmitters. MNRI may facilitate neurodevelopment, build stress resiliency, neuroplasticity, and optimal learning opportunity. There have been no reported side effects of MNRI treatments.

scholarly journals Is there a role of gut microbiota in mental health?

2020 ◽  
Vol 9 ◽  
Author(s):  
Klaus W. Lange ◽  
Katharina M. Lange ◽  
Yukiko Nakamura ◽  
Shigehiko Kanaya
Keyword(s):  
Mental Health ◽  
Mental Disorders ◽  
Gut Microbiota ◽  
Large Scale ◽  
Mental States ◽  
Autism Spectrum ◽  
Clinical Findings ◽  
Intestinal Microbes ◽  
Brain Functions ◽  
Intervention Trials

Research on the interaction between gut microbiota and the brain may have implications for our understanding of brain function, cognition, behavior and mental health. The literature on gut microbiota and its role in the pathophysiology and potential treatment of mental disorders has proliferated in recent years. Several neurodevelopmental disorders, including autism spectrum disorders, schizophrenia and attention-deficit/hyperactivity disorder, have been linked to the gut microbiota. The present perspective discusses the promise and pitfalls of gut microbiota research in relation to mental health. The manipulation of intestinal microbes in animals has revealed connections between gut microbiota and both normal and pathological brain functions. The hope fueling this research is that gut microbiota could be harnessed to prevent and treat mental disorders. The links observed between an imbalance of gut microbiota and impaired behavioral and mental states in humans are correlational. It is therefore essential to establish cause and effect relationships. No distinct gut microbiota patterns linked to different mental disorders have yet been identified. Large-scale, longitudinal trials need to examine whether the gut microbiota is a valid therapeutic target for mental disorders and whether pre-clinical findings and initial results of intervention trials (e.g., administration of probiotics) are of clinical relevance.

Integrative Analysis of shared Genetic Pathogenesis by Autism Spectrum Disorder and Obsessive-Compulsive Disorder

2019 ◽  
Author(s):  
Dongbai Liu ◽  
Hongbao Cao ◽  
Kamil Can Kural ◽  
Qi Fang ◽  
Fuquan Zhang
Keyword(s):  
Obsessive Compulsive Disorder ◽  
Large Scale ◽  
Genetic Network ◽  
Autism Spectrum ◽  
P Value ◽  
Genetic Associations ◽  
Obsessive Compulsive ◽  
Systematic Analysis ◽  
Gene Markers ◽  
Compulsive Disorder

Abstract Background Many common pathological features have been observed for both autism spectrum disorders (ASD) and obsessive-compulsive disorder (OCD). However, no systematic analysis of the common gene markers associated both ASD and OCD has been conducted so far. Results Here, two batches of large-scale literature based disease-gene relation data (updated in 2017 and 2019, respectively) and gene expression data were integrated to study the possible association between OCD and ASD at the genetic level. Genes linked to OCD and ASD present significant overlap (p-value<2.64e-39). A genetic network of over 20 genes was constructed, through which OCD and ASD may exert influence on each other. The 2017-based analysis suggested six potential common risk genes for OCD and ASD (CDH2, ADCY8, APOE, TSPO, TOR1A, and OLIG2), and the 2019-based study identified two more genes (DISP1 and SETD1A). Notably, the gene APOE identified by the 2017-based analysis has been implicated to have an association with ASD in a recently study (2018) with DNA methylation analysis. Conclusions Our results support the possible complex genetic associations between OCD and ASD. Genes linked to one disease is worthy of further investigation as potential risk factors for the other.

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