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SummaryA child’s socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric-or unimodal analyses that downplay the probable scenario that separate sources of variability, from numerous independent biological pathways, are the basis for SES-related cortical differences in youth. To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology. Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC. In conclusion, structural brain MRI is sensitive to SES-related variability in childhood, and in low-income families, having a more developed cortex across MRI metrics, is beneficial for mental health.

BackgroundResearch has demonstrated associations between pubertal development and brain maturation. However, existing studies have been limited by small samples, cross-sectional designs, and inconclusive findings regarding coupling directionality and sex differences.MethodsWe examined the longitudinal temporal coupling of puberty status assessed using the Pubertal Development Scale (PDS) and magnetic resonance imaging (MRI)-based brain grey and white matter structure. Our sample consisted of 8,896 children and adolescents at baseline (mean age = 9.9) and 6,099 at follow-up (mean age = 11.9) from the Adolescent Brain and Cognitive Development (ABCD) Study.ResultsApplying multigroup Bivariate Latent Change Score (BLCS) models, we found that baseline pubertal status predicted the rate of change in cortical thickness among females only, while it predicted rate of change in cortical surface area for both males and females, with a significantly stronger association for females. For cortical surface area, we also found a correlation between baseline pubertal status and area and co-occurring changes over time, with both associations present in males only. Diffusion tensor imaging (DTI) analysis revealed correlated change between pubertal status and fractional anisotropy (FA) for both males and females, but no significant associations for mean diffusivity (MD).ConclusionsOur results suggest that pubertal status in early adolescence predicts cortical maturation, and that the strength of the associations differ between sex. Further research is needed to understand how the associations between puberty and brain maturation are related to environmental and lifestyle factors, and the impact on mental health.

Background: Increased intraindividual variability (IIV) in reaction times (RT) has been suggested as a key cognitive and behavioral marker of attention problems, but findings for other dimensions of psychopathology are less consistent. Moreover, while studies have linked IIV to brain white matter microstructure, large studies testing the robustness of these associations are needed. Methods: We utilized data from the Adolescent Brain Cognitive Development (ABCD) Study baseline assessment to test the associations between IIV and psychopathology (n=8622, age=8.9-11.1), and between IIV and white matter microstructure (n=7958, age=8.9-11.1). IIV was investigated using an ex-Gaussian distribution analysis of RTs in correct response go trials in the stop signal task. Psychopathology was measured by the Child Behavior Checklist and a bifactor structural equation model was performed to extract a general p-factor and specific factors reflecting internalizing, externalizing, and attention problems. To investigate white matter microstructure, fractional anisotropy (FA) and mean (MD), axial (AD), and radial diffusivity (RD) were examined in 23 atlas-based tracts. Results: Increased IIV in both short and long reaction times was positively associated with the specific attention problems factor (Cohen’s d=.13 and d=.15). Increased IIV in long reaction times was also positively associated with RD in the left and right corticospinal tract (d=.12 (both tracts)). Conclusions: Using a large sample and a data-driven dimensional approach to psychopathology, the results provide novel evidence for a small but specific association between IIV and attention problems in children and supports previous findings on the relevance of white matter microstructure for IIV.

The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders., The deviation between chronological age and age predicted using brain MRI is a marker of overall brain health. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF). We found that, combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders.

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer’s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields’ genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p –16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

BackgroundCerebral myeloarchitecture shows substantial development across childhood and adolescence, and aberrations in these trajectories are relevant for a range of mental disorders. Differential myelination between intracortical and subjacent white matter can be approximated using signal intensities in T1-weighted magnetic resonance images (MRI).MethodsTo test the sensitivity of gray/white matter contrast (GWC) to age and individual differences in psychopathology and general cognitive ability in youth (8-23 years), we formed data-driven psychopathology and cognitive components using a large population-based sample, the Philadelphia Neurodevelopmental Cohort (PNC) (n=6487, 52% females). We then tested for associations with regional GWC defined by an independent component analysis (ICA) in a subsample with available MRI data (n=1467, 53% females).ResultsThe analyses revealed a global GWC component, which showed an age-related decrease from late childhood and across adolescence. In addition, we found regional anatomically meaningful components with differential age associations explaining variance beyond the global component. When accounting for age and sex, both higher symptom levels of anxiety or prodromal psychosis and lower cognitive ability were associated with higher GWC in insula and cingulate cortices and with lower GWC in pre- and postcentral cortices. We also found several additional regional associations with anxiety, prodromal psychosis and cognitive ability.ConclusionIndependent modes of GWC variation are sensitive to global and regional brain developmental processes, possibly related to differences between intracortical and subjacent white matter myelination, and individual differences in regional GWC are associated with both mental health and general cognitive functioning.

This project contains a preregistration of secondary data analysis for a research project on the complex links between the genetics influence on the wellbeing spectrum, brain structure, and risk for psychopathology in children.

Through dynamic transactional processes between genetic and environmental factors, childhood and adolescence involve reorganization and optimization of the cerebral cortex. The cortex and its development plays a crucial role for prototypical human cognitive abilities. At the same time, many common mental disorders appear during these critical phases of neurodevelopment. Magnetic resonance imaging (MRI) can indirectly capture several multifaceted changes of cortical macro- and microstructure, of high relevance to further our understanding of the neural foundation of cognition and mental health. Great progress has been made recently in mapping the typical development of cortical morphology. Moreover, newer less explored MRI signal intensity and specialized quantitative T2 measures have been applied to assess microstructural cortical development. We review recent findings of typical postnatal macro- and microstructural development of the cerebral cortex from early childhood to young adulthood. We cover studies of cortical volume, thickness, area, gyrification, T1-weighted (T1w) tissue contrasts such a grey/white matter contrast, T1w/T2w ratio, magnetization transfer and myelin water fraction. Finally, we integrate imaging studies with cortical gene expression findings to further our understanding of the underlying neurobiology of the developmental changes, bridging the gap between ex vivo histological- and in vivo MRI studies.

Human brain development involves spatially and temporally heterogeneous changes, detectable across a wide range of magnetic resonance imaging (MRI) measures. Investigating the interplay between multimodal MRI and polygenic scores (PGS) for personality traits associated with mental disorders in youth may provide new knowledge about typical and atypical neurodevelopment. We derived independent components across cortical thickness, cortical surface area, and grey/white matter contrast (GWC) (n = 2596, 3–23 years), and tested for associations between these components and age, sex and-, in a subsample (n = 878), PGS for neuroticism. Age was negatively associated with a single-modality component reflecting higher global GWC, and additionally with components capturing common variance between global thickness and GWC, and several multimodal regional patterns. Sex differences were found for components primarily capturing global and regional surface area (boys > girls), but also regional cortical thickness. For PGS for neuroticism, we found weak and bidirectional associations with a component reflecting right prefrontal surface area. These results indicate that multimodal fusion is sensitive to age and sex differences in brain structure in youth, but only weakly to polygenic load for neuroticism.

INTRODUCTIONAccumulating evidence shows that patients with anorexia nervosa (AN) have globally reduced brain mass, including lower cortical volume and thickness, which largely normalizes following weight restoration. The dynamic underlying mechanisms for these processes are unknown, and how age and severity of emaciation are associated with brain morphology in AN is poorly understood. We investigated associations of age, body mass index (BMI) and biochemical parameters with brain morphology in a large sample of patients in treatment. METHODS: We included 85 patients (94% female) aged 12-48 (mean = 23) years with clinical and quality controlled magnetic resonance imaging (MRI) data. T1-weighted MRI images, clinical characteristics, and biochemical parameters were retrospectively collected from hospital records. Brain morphology was measured using FreeSurfer, and associations were investigated using regression models and correlations. RESULTS: Controlling for BMI, age showed significant associations with brain morphology generally concordant with known typical brain developmental patterns. Controlling for age, BMI showed significant positive associations with cortical volume and thickness. There were no significant interaction effects between age and BMI. None of the biochemical parameters correlated significantly with brain morphology.DISCUSSIONOur findings suggest the presence of normal neurodevelopmental patterns in AN and highlight the value of considering age-related effects on brain morphology. Importantly, we showed that severity of emaciation is related to brain morphology reductions, underscoring the importance of weight restoration. More studies are needed to shed light on potential biochemical mechanisms associated with brain alterations in AN.

Sleep is a universal phenomenon necessary for maintaining homeostasis and function across a range of organs. Lack of sleep has severe health-related consequences affecting whole-body functioning, yet no other organ is as severely affected as the brain. The neurophysiological mechanisms underlying these deficits are poorly understood. Here, we characterize the dynamic changes in brain connectivity profiles inflicted by sleep deprivation and how they deviate from regular daily variability. To this end, we obtained functional magnetic resonance imaging data from 60 young, adult male participants, scanned in the morning and evening of the same day and again the following morning. 41 participants underwent total sleep deprivation before the third scan, whereas the remainder had another night of regular sleep. Sleep deprivation strongly altered the connectivity of several resting-state networks, including dorsal attention, default mode, and hippocampal networks. Multivariate classification based on connectivity profiles predicted deprivation state with high accuracy, corroborating the robustness of the findings on an individual level. Finally, correlation analysis suggested that morning-to-evening connectivity changes were reverted by sleep (control group) – a pattern which did not occur after deprivation. We conclude that both, a day of waking and a night of sleep deprivation dynamically alter the brain functional connectome.

The restructuring and optimization of the cerebral cortex from early childhood and through adolescence is an essential feature of human brain development, underlying immense cognitive improvements. Beyond established morphometric cortical assessments, the T1w/T2w ratio quantifies partly separate biological processes, and might inform models of typical neurocognitive development and developmental psychopathology. In the present study, we computed vertex‐wise T1w/T2w ratio across the cortical surface in 621 youths (3–21 years) sampled from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and tested for associations with individual differences in age, sex, and both general and specific cognitive abilities. The results showed a near global linear age‐related increase in T1w/T2w ratio across the brain surface, with a general posterior to anterior increasing gradient in association strength. Moreover, results indicated that boys in late adolescence had regionally higher T1w/T2w ratio as compared to girls. Across individuals, T1w/T2w ratio was negatively associated with general and several specific cognitive abilities mainly within anterior cortical regions. Our study indicates age‐related differences in T1w/T2w ratio throughout childhood, adolescence, and young adulthood, in line with the known protracted myelination of the cortex. Moreover, the study supports T1w/T2w ratio as a promising surrogate measure of individual differences in intracortical brain structure in neurodevelopment., Our study indicates age‐related differences in T1w/T2w ratio throughout youth, in line with the known protracted myelination of the cortex. Moreover, the study supports T1w/T2w ratio as a promising surrogate measure of individual differences in intracortical brain structure in neurodevelopment.

BACKGROUND: Accumulating evidence supports cerebellar involvement in mental disorders, such as schizophrenia, bipolar disorder, depression, anxiety disorders, and attention-deficit/hyperactivity disorder. However, little is known about the cerebellum in developmental stages of these disorders. In particular, whether cerebellar morphology is associated with early expression of specific symptom domains remains unclear.METHODS: We used machine learning to test whether cerebellar morphometric features could robustly predict general cognitive function and psychiatric symptoms in a large and well-characterized developmental community sample centered on adolescence (Philadelphia Neurodevelopmental Cohort, n = 1401, age 8-23 years).RESULTS: Cerebellar morphology was associated with both general cognitive function and general psychopathology (mean correlations between predicted and observed values: r = .20 and r = .13; p CONCLUSIONS: The robust associations with psychiatric symptoms in the age range when these typically emerge highlight the cerebellum as a key brain structure in the development of severe mental disorders.

Background The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are known to be differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often found to be impaired in individuals with brain disorders associated with reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given these structural and functional differences, we sought to characterize the subfields’ shared and specific genetic architecture. Methods T1-images (n= 17418, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We calculated the SNP-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features, and with AD and schizophrenia. We further ran a genome-wide association analysis on each subfield, correcting for total hippocampal volume. All analyses included age, age2, sex, and intracranial volume as covariates. Results Volumes of all subfields were heritable (h2 ranging from .15 to .29, all p< 2.2 * 10-9) and clustered together (genetic correlations Rg>.41), compared to other brain features. The subiculum and the hippocampal-amygdalar transition area (HATA) showed significant genetic correlation with AD and schizophrenia, respectively. We found 14 independent whole-genome significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were annotated to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. Conclusions Hippocampal subfields have partly distinct genetic determinants, associated with specific biological processes and traits. Taking into account this specificity may aid in furthering our understanding of hippocampal neurobiology and associated disorders.

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer’s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields’ genetic architecture. T1-weighted brain scans (n=21297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, covarying for total hippocampal volume. We further calculated the single nucleotide polymorphism (SNP)-based heritability of twelve subfields, as well as their genetic correlation with each other, with other structural brain features, and with AD and schizophrenia. All outcome measures were corrected for age, sex, and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from .14 to .27, all p< 1×10-16) and clustered together based on their genetic correlations compared to other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

ImportanceAccumulating evidence supports cerebellar involvement in mental disorders such as schizophrenia, bipolar disorder, depression, anxiety disorders and attention-deficit hyperactivity disorder. However, little is known about cerebellar involvement in the developmental stages of these disorders. In particular, whether cerebellar morphology is associated with early expression of specific symptom domains remains unclear.ObjectiveTo determine the robustness and specificity of associations between cerebellar morphology, general cognitive function, general psychopathology and sub-clinical psychiatric symptom domains in adolescence.Design, setting and participantsAssessment of parametric structure-function associations between MR-based brain morphometric features and data-driven cognitive and clinical phenotypes in the Philadelphia Neurodevelopmental Cohort (N=1401, age-range: 8 - 23).Main outcomes and measuresRobust prediction of cognitive and clinical symptom domain scores from cerebellar, subcortical and cerebro-cortical brain features using machine learning with 10-fold internal cross-validation and permutation-based statistical inference.ResultsCerebellar morphology predicted both general cognitive function and general psychopathology (mean Pearson correlation coefficients between predicted and observed values: r = .20 and r = .13, respectively; corrected p-values < .0009). Analyses of specific sub-clinical symptom domains revealed significant associations with rates of norm-violating behavior (r = .17; p < .0009), prodromal psychotic symptoms (r = .12; p < .0009) and anxiety symptoms (r = .09; p =.0117). In contrast, we observed no significant associations between cerebellar features and the severity of attention deficits, depressive, manic or obsessive-compulsive symptoms (all rs =< .03, all ps => .1). Associations with norm-violating behavior and prodromal psychotic symptoms were stronger for the cerebellum than for subcortical and cerebro-cortical regions, while anxiety and general cognitive function were related to more global brain morphology patterns. The association between cerebellar volume and prodromal psychotic symptoms, and to a lesser extent norm violating behavior, remained significant when adjusting for potentially confounding factors such as general cognitive function, general psychopathology, parental education level and use of psychoactive substances.Conclusions and relevanceThe robust associations with sub-clinical psychiatric symptoms in the age range when these typically emerge highlight the cerebellum as a key brain structure in the development of severe mental disorders.Key pointsQuestionsIs cerebellar morphology associated with sub-clinical psychiatric symptoms in adolescence? Do such associations show symptom domain specificity or do they rather constitute a marker of general psychopathology?FindingsMachine learning utilizing cerebellar morphology features significantly predicted the severity of prodromal psychotic symptoms, norm-violating behavior and anxiety, but not attention deficits, depressive, manic or obsessive-compulsive sub-clinical symptoms. Associations with prodromal psychotic symptoms were stronger for the cerebellum than for cerebral subcortical and cerebro-cortical regions, and remained significant when adjusting for several potentially confounding factors.MeaningThe cerebellum appears to play a key role in the development of severe mental illness.

Background: Lower vitamin D levels are found in people with schizophrenia and depressive disorders, and also associated with neuroimaging abnormalities such as reduced brain volume in both animals and humans. Reduced whole brain and increased ventricular volume are also systematically reported in schizophrenia. Even though vitamin D deficiency has been proposed as a risk mechanism for schizophrenia there exist no studies to date of the association between vitamin D levels and brain volume in this population. Therefore, we investigated the relationship between vitamin D levels and brain phenotypes in psychotic disorders, and assessed possible interactions with genetic variants in vitamin D receptor (VDR) and other genetic variants that play a role in vitamin D levels in the body. Methods: Our sample consisted of 83 psychosis patients and 101 healthy controls. We measured vitamin D levels as serum 25-hydroxyvitamin D. All participants were genotyped and neuroimaging conducted by structural magnetic resonance imaging. Results: Vitamin D levels were significantly positively associated with peripheral grey matter volume in patients (β 860.6; 95% confidence interval (CI) 333.4–1466, p < .003). A significant interaction effect of BSML marker (rs1544410) was observed to mediate the association between patient status and both white matter volume (β 23603.3; 95% CI 2732.8–48708.6, p < .05) and whole brain volume (β 46670.6, 95% CI 8817.8–93888.3, p < .04). Vitamin D did not predict ventricular volume, which rather was associated with patient status (β 4423.3, 95% CI 1583.2–7267.8p < .002) and CYP24A1 marker (rs6013897) (β 2491.5, 95% CI 269.7–4978.5, p < .04). Conclusions: This is the first study of the association between vitamin D levels and brain volume in patients with psychotic disorders that takes into account possible interaction with genetic polymorphisms. The present findings warrant replication in independent samples. publishedVersion

Heart rate variability (HRV) has become an increasingly popular index of cardiac autonomic control in the biobehavioral sciences due to its relationship with mental illness and cognitive traits. However, the intraindividual stability of HRV in response to sleep and diurnal disturbances, which are commonly reported in mental illness, and its relationship with executive function are not well understood. Here, in 40 healthy adult males we calculated high frequency HRV—an index of parasympathetic nervous system (PNS) activity—using pulse oximetry during brain imaging, and assessed attentional and executive function performance in a subsequent behavioral test session at three time points: morning, evening, and the following morning. Twenty participants were randomly selected for total sleep deprivation whereas the other 20 participants slept as normal. Sleep deprivation and morning-to-night variation did not influence high frequency HRV at either a group or individual level; however, sleep deprivation abolished the relationship between orienting attention performance and HRV. We conclude that a day of wake and a night of laboratory-induced sleep deprivation do not alter supine high frequency HRV in young healthy male adults.

Background Elucidating the neurobiological effects of sleep and waking remains an important goal of the neurosciences. Recently, animal studies indicated that sleep is important for cell membrane and myelin maintenance in the brain and that these structures are particularly susceptible to insufficient sleep. Here, we tested the hypothesis that a day of waking and sleep deprivation would be associated with changes in diffusion tensor imaging (DTI) indices of white matter microstructure sensitive to axonal membrane and myelin alterations. Methods Twenty-one healthy adult males underwent DTI in the morning [7:30AM; time point (TP)1], after 14 hours of waking (TP2), and then after another 9 hours of waking (TP3). Whole brain voxel-wise analysis was performed with tract based spatial statistics. Results A day of waking was associated with widespread increases in white matter fractional anisotropy, which were mainly driven by radial diffusivity reductions, and sleep deprivation was associated with widespread fractional anisotropy decreases, which were mainly explained by reductions in axial diffusivity. In addition, larger decreases in axial diffusivity after sleep deprivation were associated with greater sleepiness. All DTI changes remained significant after adjusting for hydration measures. Conclusions This is the first DTI study of sleep deprivation in humans. Although previous studies have observed localized changes in DTI indices of cerebral microstructure over the course of a few hours, further studies are needed to confirm widespread DTI changes within hours of waking and to clarify whether such changes in white matter microstructure serve as neurobiological substrates of sleepiness.

Combining imaging modalities and metrics that are sensitive to various aspects of brain structure and maturation may help identify individuals that show deviations in relation to same-aged peers, and thus benefit early-risk-assessment for mental disorders. We used one timepoint multimodal brain imaging, cognitive, and questionnaire data from 1280 8-21-year-olds from the Philadelphia Neurodevelopmental Cohort. We estimated age-related gray and white matter properties and estimated individual deviation scores using normative modeling. Next, we looked for associations between the estimated deviation scores, and with psychopathology domain scores and cognition. More negative deviations in DTI-based fractional anisotropy (FA) and the first principal eigenvalue of the diffusion tensor (L1) were associated with higher scores on psychosis positive and prodromal symptoms and a general psychopathology factor. A more negative deviation in cortical thickness (CT) was associated with a higher general psychopathology score. Negative deviations in global FA, surface area, L1 and CT were also associated with poorer cognitive performance. No robust associations were found between the deviation scores based on CT and DTI. The low correlations between the different multimodal MRI based deviation scores support that brain maturation is highly heterogenous and suggest that psychopathological burden in adolescence may map onto partly distinct neurobiological features.

BackgroundSchizophrenia and bipolar disorder share genetic risk factors and one possible illness mechanism is abnormal myelination. T1-weighted magnetic resonance imaging (MRI) tissue intensities are sensitive to myelin content. Therefore, the contrast between grey- and white-matter intensities may reflect myelination along the cortical surface.MethodMRI images were obtained from patients with schizophrenia (n = 214), bipolar disorder (n = 185), and healthy controls (n = 278) and processed in FreeSurfer. The grey/white-matter contrast was computed at each vertex as the difference between average grey-matter intensity (sampled 0–60% into the cortical ribbon) and average white-matter intensity (sampled 0–1.5 mm into subcortical white matter), normalized by their average. Group differences were tested using linear models covarying for age and sex.ResultsPatients with schizophrenia had increased contrast compared to controls bilaterally in the post- and precentral gyri, the transverse temporal gyri and posterior insulae, and in parieto-occipital regions. In bipolar disorder, increased contrast was primarily localized in the left precentral gyrus. There were no significant differences between schizophrenia and bipolar disorder. Findings of increased contrast remained after adjusting for cortical area, thickness, and gyrification. We found no association with antipsychotic medication dose.ConclusionsIncreased contrast was found in highly myelinated low-level sensory and motor regions in schizophrenia, and to a lesser extent in bipolar disorder. We propose that these findings indicate reduced intracortical myelin. In accordance with the corollary discharge hypothesis, this could cause disinhibition of sensory input, resulting in distorted perceptual processing leading to the characteristic positive symptoms of schizophrenia.