Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence.

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

Experiences and impact of psychiatric inpatient admissions far away from home: a qualitative study with young people, parents/carers and healthcare professionals.

BACKGROUND: There are significant clinical, policy and societal concerns about the impact on young people (YP), from admission to psychiatric wards far from home. However, research evidence is scarce. AIMS: To investigate the impact of at-distance admissions to general adolescent units, from the perspectives of YP, parents/carers and healthcare professionals (HCPs) including service commissioners, to inform clinical practice, service development and policy. METHOD: Semistructured interviews with purposive samples of YP aged 13-17 years (n=28) and parents/carers (n=19) across five large regions in England, and a national sample of HCPs (n=51), were analysed using a framework approach. RESULTS: There was considerable agreement between YP, parents/carers and HCPs on the challenges of at-distance admissions. YP and parents/carers had limited or no involvement in decision-making processes around admission and highlighted a lack of available information about individual units. Being far from home posed challenges with maintaining home contact and practical/financial challenges for families visiting. HCPs struggled with ensuring continuity of care, particularly around maintaining access to local clinical teams and educational support. However, some YP perceived separation from their local environment as beneficial because it removed them from unhelpful environments. At-distance admissions provided respite for some families struggling to support their child. CONCLUSIONS: At-distance admissions lead to additional distress, uncertainty, compromised continuity of care and educational, financial and other practical difficulties, some of which could be better mitigated. For a minority, there are some benefits from such admissions. CLINICAL IMPLICATIONS: Standardised online information, accessible prior to admission, is needed for all Child and Adolescent Mental Health Services units. Additional practical and financial burden placed on families needs greater recognition and consideration of potential sources of support. Policy changes should incorporate findings that at-distance or adult ward admissions may be preferable in certain circumstances.

Genetic evidence for the causal effects of C-reactive protein on self-reported habitual sleep duration.

Inflammatory responses to acute stimuli are proposed to regulate sleep, but the relationship between chronic inflammation and habitual sleep duration is elusive. Here, we study this relation using genetically predicted level of chronic inflammation, indexed by CRP and IL6 signaling, and self-reported sleep duration. By Mendelian randomization analysis, we show that elevated CRP level within <10 mg/L has a homeostatic effect that facilitates maintaining 7-8 h sleep duration per day - making short-sleepers sleep longer (p = 2.42 × 10-2) and long-sleepers sleep shorter (1.87 × 10-7); but it is not associated with the overall sleep duration (p = 0.17). This homeostatic effect replicated in an independent CRP dataset. We observed causal effects of the soluble interleukin 6 receptor and gp130 on overall sleep duration (p = 1.62 × 10-8, p = 2.61 × 10-58, respectively), but these effects disappeared when CRP effects were accounted for in the model. Using polygenic score analysis, we found that the homeostatic effect of CRP on sleep duration stems primarily from the genetic variants within the CRP gene region: when genetic variants outside of this region were used to predict CRP levels, the opposite direction of effect was observed. In conclusion, we show that elevated CRP level may causally facilitate maintaining an optimal sleep duration that is beneficial to health, thus updating our current knowledge of immune regulation on sleep.

Subtypes of brain change in aging and their associations with cognition and Alzheimer's disease biomarkers.

Structural brain changes underly cognitive changes in older age and contribute to inter-individual variability in cognition. Here, we assessed how changes in cortical thickness, surface area, and subcortical volume, are related to cognitive change in cognitively unimpaired older adults using structural magnetic resonance imaging (MRI) data-driven clustering. Specifically, we tested (1) which brain structural changes over time predict cognitive change in older age (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers phosphorylated tau (p-tau) and amyloid-ß (Aß42), and (3) the degree of overlap between clusters derived from different structural features. In total 1899 cognitively healthy older adults (50 - 93 years) were followed up to 16 years with neuropsychological and structural MRI assessments, a subsample of which (n = 612) had CSF p-tau and Aß42 measurements. We applied Monte-Carlo Reference-based Consensus clustering to identify subgroups of older adults based on structural brain change patterns over time. Four clusters for each brain feature were identified, representing the degree of longitudinal brain decline. Each brain feature provided a unique contribution to brain aging as clusters were largely independent across modalities. Cognitive change and baseline cognition were best predicted by cortical area change, whereas higher levels of p-tau and Aß42 were associated with changes in subcortical volume. These results provide insights into the link between changes in brain morphology and cognition, which may translate to a better understanding of different aging trajectories.

Effectiveness and cost-effectiveness of online recorded recovery narratives in improving quality of life for people with non-psychotic mental health problems: a pragmatic randomized controlled trial.

Narratives describing first-hand experiences of recovery from mental health problems are widely available. Emerging evidence suggests that engaging with mental health recovery narratives can benefit people experiencing mental health problems, but no randomized controlled trial has been conducted as yet. We developed the Narrative Experiences Online (NEON) Intervention, a web application providing self-guided and recommender systems access to a collection of recorded mental health recovery narratives (n=659). We investigated whether NEON Intervention access benefited adults experiencing non-psychotic mental health problems by conducting a pragmatic parallel-group randomized trial, with usual care as control condition. The primary endpoint was quality of life at week 52 assessed by the Manchester Short Assessment (MANSA). Secondary outcomes were psychological distress, hope, self-efficacy, and meaning in life at week 52. Between March 9, 2020 and March 26, 2021, we recruited 1,023 participants from across England (the target based on power analysis was 994), of whom 827 (80.8%) identified as White British, 811 (79.3%) were female, 586 (57.3%) were employed, and 272 (26.6%) were unemployed. Their mean age was 38.4±13.6 years. Mood and/or anxiety disorders (N=626, 61.2%) and stress-related disorders (N=152, 14.9%) were the most common mental health problems. At week 52, our intention-to-treat analysis found a significant baseline-adjusted difference of 0.13 (95% CI: 0.01-0.26, p=0.041) in the MANSA score between the intervention and control groups, corresponding to a mean change of 1.56 scale points per participant, which indicates that the intervention increased quality of life. We also detected a significant baseline-adjusted difference of 0.22 (95% CI: 0.05-0.40, p=0.014) between the groups in the score on the "presence of meaning" subscale of the Meaning in Life Questionnaire, corresponding to a mean change of 1.1 scale points per participant. We found an incremental gain of 0.0142 quality-adjusted life years (QALYs) (95% credible interval: 0.0059 to 0.0226) and a £178 incremental increase in cost (95% credible interval: -£154 to £455) per participant, generating an incremental cost-effectiveness ratio of £12,526 per QALY compared with usual care. This was lower than the £20,000 per QALY threshold used by the National Health Service in England, indicating that the intervention would be a cost-effective use of health service resources. In the subgroup analysis including participants who had used specialist mental health services at baseline, the intervention both reduced cost (-£98, 95% credible interval: -£606 to £309) and improved QALYs (0.0165, 95% credible interval: 0.0057 to 0.0273) per participant as compared to usual care. We conclude that the NEON Intervention is an effective and cost-effective new intervention for people experiencing non-psychotic mental health problems.

'Far Away from Home': adolescent inpatient admissions far from home, out of area or to adult wards: a national surveillance study.

BACKGROUND: The increasing prevalence and acuity of mental disorders among children and adolescents have placed pressure on services, including inpatient care, and resulted in young people being admitted at-distance or to adult wards. Little empirical research has investigated such admissions. OBJECTIVE: To determine the incidence, clinical characteristics and 6-month outcomes of patients aged 13-17 years old admitted at-distance (>50 miles from home or out of region) to general adolescent psychiatric wards or to adult psychiatric wards. METHODS: Surveillance over 13 months (February 2021-February 2022) using the Child and Adolescent Psychiatry Surveillance System including baseline and 6-month follow-up questionnaires. FINDINGS: Data were collected about 290 admissions (follow-up rate 99% (288 of 290); sample were 73% female, mean age 15.8 years). The estimated adjusted yearly incidence of at-distance admission was 13.7-16.9 per 100 000 young people 13-17 years old. 38% were admitted >100 miles from home and 8% >200 miles. The most common diagnoses at referral were depression (34%) and autism spectrum disorder (20%); other common referral concerns included suicide risk (80%), emotional dysregulation (53%) and psychotic symptoms (22%). Over two-fifths (41%) waited ≥1 week for a bed, with 55% waiting in general hospital settings. At 6-month follow-up, 20% were still in hospital, the majority in at-distance placements. CONCLUSIONS: At-distance and adult ward admissions for patients aged <18 remain an ongoing challenge for healthcare provision and have an impact on acute hospital resource use. CLINICAL IMPLICATIONS: Long waits in non-specialist settings increase pressure across the healthcare system, highlighting the need to improve local service provision and commissioning to reflect identified clinical needs.

Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex.

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the developmental timing of asymmetry and the extent to which it arises through genetic and later influences in childhood. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in seven datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry is low-moderately heritable (max h2SNP ~19%) and correlates phenotypically and genetically in specific regions, indicating coordinated development of asymmetries partly through genes. In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises early in life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

Genetic architecture of brain age and its causal relations with brain and mental disorders.

The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.

Circulating S100B levels at birth and risk of six major neuropsychiatric or neurological disorders: a two-sample Mendelian Randomization Study.

Circulating levels of the astrocytic marker S100B have been associated with risk of neuropsychiatric or neurological disorders. However, reported effects have been inconsistent, and no causal relations have yet been established. We applied two-sample Mendelian Randomization (MR) on the association statistics from genome-wide association studies (GWAS) for circulating S100B levels measured 5-7 days after birth (the iPSYCH sample) and in an older adult sample (mean age, 72.5 years; the Lothian sample), upon those derived from major depression disorder (MDD), schizophrenia (SCZ), bipolar disorder (BIP), autism spectral disorder (ASD), Alzheimer's disease (AD), and Parkinson's disease (PD). We studied the causal relations in the two S100B datasets for risk of these six neuropsychiatric disorders. MR suggested increased S100B levels 5-7 days after birth to causally increase the risk of MDD (OR = 1.014; 95%CI = 1.007-1.022; FDR-corrected p = 6.43×10-4). In older adults, MR suggested increased S100B levels to have a causal relation to the risk of BIP (OR = 1.075; 95%CI = 1.026-1.127; FDR-corrected p = 1.35×10-2). No significant causal relations were found for the other five disorders. We did not observe any evidence for reverse causality of these neuropsychiatric or neurological disorders on altered S100B levels. Sensitivity analyses using more stringent SNP-selection criteria and three alternative MR models suggested the results are robust. Altogether, our findings imply a small cause-effect relation for the previously reported associations of S100B and mood disorders. Such findings may provide a novel avenue for the diagnosis and management of disorders.

Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates.

It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.

A Self-management App for People Living With Mild Dementia (PRIDE): Protocol for a Pre-Post Feasibility Study.

BACKGROUND: With the rapid increase in the prevalence of dementia in the United Kingdom and beyond, the emotional, social, and economic burden on individuals, families, and health care services continues to rise. Currently, interventions that enable people living with dementia to better manage their condition and achieve a good quality of life are needed. OBJECTIVE: This study aimed to explore how the Promoting Independence in Dementia (PRIDE) app can promote and support the self-management of people living with mild dementia. METHODS: Feasibility of a pre-post study design incorporating the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework will be studied. We will use up to 6 National Health Service Trusts as research sites and the Join Dementia Research website and accept self-referrals to recruit 60 to 90 people living with mild dementia. Participants will complete the PRIDE app intervention over 8 weeks with support from a dementia adviser facilitator. Measures exploring mood, physical well-being, and quality of life will be collected at baseline and at follow-ups at 3 and 6 months. Facilitators and National Health Service staff will be invited to complete interviews shortly after the intervention phase. RESULTS: Data collection began in June 2021 and is predicted to cease by the end of August 2022. Analysis of the quantitative measures will explore the impact of the PRIDE app on participants' independence, mood, and quality of life. Interview data will discuss participant experiences, how the use of the app affected them, and if it has the potential to be successfully implemented and maintained in dementia services. CONCLUSIONS: This study will show the potential reach, effectiveness, and adoption of the PRIDE app intervention in the lives of people with mild dementia. The findings from this study will inform future research on the PRIDE app and any further developments to improve its effectiveness. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33881.

Deep neural networks learn general and clinically relevant representations of the ageing brain.

The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.

Service user experiences of care recommendations from the 2014 NICE guideline for bipolar disorder: a survey.

Clinical guidelines for mental health disorders produced by the National Institute of Care Excellence (NICE) emphasise a recovery-based approach clinical care with collaborative decision-making. The aim of the study was to explore service user experience of collaborative decision-making and recovery focussed care in relation to a NICE clinical guideline for bipolar disorder four years after publication. Participants with a clinical diagnosis of bipolar disorder were recruited from adult mental health services in four specialist mental health NHS Trusts through health professional or self-referral following advertisement. An online or written survey was designed with service user input to cover 40 NICE recommendations on recovery based or collaborative care. Participants completed the survey anonymously and independent of any health professional involvement. Of 222 participants, 72 (33.5%) reported to a great extent care was delivered in line with a positive recovery message; 55 (25.5%) reported that not much or no care was recovery based. Only four items (10%) on medication or the offer of crisis services were endorsed as collaborative decision-making with a health professional by >70% service users. Most decision-making in relation to the NICE clinical guideline for bipolar disorder was not delivered collaboratively and only some care was recovery focussed.

How do recorded mental health recovery narratives create connection and improve hopefulness?

BACKGROUND: Mental health recovery narratives are an active ingredient of recovery-oriented interventions such as peer support. Recovery narratives can create connection and hope, but there is limited evidence on the predictors of impact. AIMS: The aim of this study was to identify characteristics of the narrator, narrative content and participant which predict the short-term impact of recovery narratives on participants. METHOD: Independent studies were conducted in an experimental (n = 40) and a clinical setting (n = 13). In both studies, participants with mental health problems received recorded recovery narratives and rated impact on hopefulness and connection. Predictive characteristics were identified using multi-level modelling. RESULTS: The experimental study found that narratives portraying a narrator as living well with mental health problems that is intermediate between no and full recovery, generated higher self-rated levels of hopefulness. Participants from ethnic minority backgrounds had lower levels of connection with narrators compared to participants from a white background, potentially due to reduced visibility of a narrator's diversity characteristics. CONCLUSIONS: Narratives describing partial but not complete recovery and matching on ethnicity may lead to a higher impact. Having access to narratives portraying a range of narrator characteristics to maximise the possibility of a beneficial impact on connection and hopefulness.

Associations of circulating C-reactive proteins, APOE ε4, and brain markers for Alzheimer's disease in healthy samples across the lifespan.

The apolipoprotein E gene ε4 allele (APOE ε4) and higher circulating level of C-reactive protein (CRP) have been extensively investigated as risk factors for Alzheimer's disease (AD). Paradoxically, APOE ε4 has been associated with lower levels of blood CRP in middle-aged and older populations. However, few studies have investigated this intriguing relation and its impact on neurological markers for AD in younger ages, nor across the whole lifespan. Here, we examine associations of blood CRP levels, APOE ε4, and biomarkers for AD in a cognitively healthy lifespan cohort (N up to 749; 20-81 years of age) and replicate the findings in UK Biobank (N = 304 322; 37-72 years of age), the developmental ABCD study (N = 10 283; 9-11 years of age), and a middle-aged sample (N = 339; 40-65 years of age). Hippocampal volume, brain amyloid-ß (Aß) plaque levels, cerebrospinal fluid (CSF) levels of Aß and tau species, and neurofilament protein light protein (NFL) were used as AD biomarkers in subsamples. In addition, we examined the genetic contribution to the variation of CRP levels over different CRP ranges using polygenic scores for CRP (PGS-CRP). Our results show APOE ε4 consistently associates with low blood CRP levels across all age groups (p < 0.05). Strikingly, both ε4 and PGS-CRP associated mainly with blood CRP levels within the low range (<5mg/L). We then show both APOE ε4 and high CRP levels associate with smaller hippocampus volumes across the lifespan (p < 0.025). APOE ε4 was associated with high Aß plaque levels in the brain (FDR-corrected p = 8.69x10-4), low levels of CSF Aß42 (FDR-corrected p = 6.9x10-2), and lower ratios of Aß42 to Aß40 (FDR-corrected p = 5.08x10-5). Blood CRP levels were weakly correlated with higher ratio of CSF Aß42 to Aß40 (p = 0.03, FDR-corrected p = 0.4). APOE ε4 did not correlate with blood concentrations of another 9 inflammatory cytokines, and none of these cytokines correlated with AD biomarkers. CONCLUSION: The inverse correlation between APOEε 4 and blood CRP levels existed before any pathological AD biomarker was observed, and only in the low CRP level range. Thus, we suggest to investigate whether APOEε 4 can confer risk by being associated with a lower inflammatory response to daily exposures, possibly leading to greater accumulation of low-grade inflammatory stress throughout life. A lifespan perspective is needed to understand this relationship concerning risk of developing AD.

Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change.

Brain age is a widely used index for quantifying individuals' brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

Scientists who study the brain and aging are keen to find an effective way to measure brain health, which could help identify people at risk for dementia or memory problems. One popular marker is 'brain age'. This measurement uses a brain scan to estimate a person's chronological age, then compares the estimated brain age to the person's actual age to determine whether their brain is aging faster or slower than expected for their age. However, since brain age relies on one brain scan taken at one point in time, it is not clear whether it really measures brain aging or if it might capture brain differences that have been present throughout the individual's life. Studies comparing individual brain scans over several years would be necessary to know for sure. Now, Vidal-Piñeiro et al. show that the brain-age measurement does not reflect faster brain aging. In the experiments, the researchers compared repeated brain scans of thousands of individuals over 40 years of age. The experiments showed that deviations from normative brain age detected in a single scan reflected early life differences more than changes in the brain over time. For example, people with older-looking brains were more likely to have had a low birth weight or to have a combination of genes associated with having an older looking brain. Vidal-Piñeiro et al. show that brain age mostly reflects a pre-existing brain condition rather than brain aging. The experiments also suggest that genetics and early brain development likely have a strong impact on brain health throughout life. Future studies trying to test or develop brain-aging measurements should use serial measurements to track brain changes over time.

Handedness and its genetic influences are associated with structural asymmetries of the cerebral cortex in 31,864 individuals.

Roughly 10% of the human population is left-handed, and this rate is increased in some brain-related disorders. The neuroanatomical correlates of hand preference have remained equivocal. We resampled structural brain image data from 28,802 right-handers and 3,062 left-handers (UK Biobank population dataset) to a symmetrical surface template, and mapped asymmetries for each of 8,681 vertices across the cerebral cortex in each individual. Left-handers compared to right-handers showed average differences of surface area asymmetry within the fusiform cortex, the anterior insula, the anterior middle cingulate cortex, and the precentral cortex. Meta-analyzed functional imaging data implicated these regions in executive functions and language. Polygenic disposition to left-handedness was associated with two of these regional asymmetries, and 18 loci previously linked with left-handedness by genome-wide screening showed associations with one or more of these asymmetries. Implicated genes included six encoding microtubule-related proteins: TUBB, TUBA1B, TUBB3, TUBB4A, MAP2, and NME7-mutations in the latter can cause left to right reversal of the visceral organs. There were also two cortical regions where average thickness asymmetry was altered in left-handedness: on the postcentral gyrus and the inferior occipital cortex, functionally annotated with hand sensorimotor and visual roles. These cortical thickness asymmetries were not heritable. Heritable surface area asymmetries of language-related regions may link the etiologies of hand preference and language, whereas nonheritable asymmetries of sensorimotor cortex may manifest as consequences of hand preference.