Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses.

Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants were analyzed across sites using mixed-model ANCOVAs. The meta-analysis of cortical thickness showed a converging pattern of widespread thinner cortex in fronto-parietal regions of the left hemisphere in both DSZ and NDSZ, when compared to HC. However, DSZ have more pronounced thickness abnormalities than NDSZ, mostly involving the right fronto-parietal cortices. As for surface area, NDSZ showed differences in fronto-parietal-temporo-occipital cortices as compared to HC, and in temporo-occipital cortices as compared to DSZ. Although DSZ and NDSZ show widespread overlapping regions of thinner cortex as compared to HC, cortical thinning seems to better typify DSZ, being more extensive and bilateral, while surface area alterations are more evident in NDSZ. Our findings demonstrate for the first time that DSZ and NDSZ are characterized by different neuroimaging phenotypes, supporting a nosological distinction between DSZ and NDSZ and point toward the separate disease hypothesis.

Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

BACKGROUND: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. METHODS: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. RESULTS: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. CONCLUSIONS: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles  and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals.

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

The relationship between measurement of in vivo brain glutamate and markers of iron metabolism: A proton magnetic resonance spectroscopy study in healthy adults.

Fundamental human studies which address associations between glutamate and iron metabolism are needed. Basic research reports associations between glutamate and iron metabolism. Human studies report sex differences in iron metabolism and glutamate concentrations, which suggest that these relationships may differ by sex. We hypothesised associations would be apparent between in vivo glutamate and peripheral markers of iron metabolism, and these associations would differ by sex. To test this, we recruited 40 healthy adults (20 men, 20 women) and measured (a) standard clinical biomarker concentrations for iron metabolism and (b) an in vivo proxy for glutamate concentration, glutamate with glutamine in relation to total creatine containing metabolites using proton magnetic resonance spectroscopy studies with a two-dimensional chemical shift imaging slice, with voxels located in bilateral dorsolateral prefrontal cortices, anterior cingulate cortices and frontal white matter. Only the female group reported significant associations between peripheral markers of iron metabolism and Glx:tCr concentration: (a) right dorsolateral prefrontal cortex Glx:tCr associated positively with serum transferrin (r = .60, p = .006) and negatively with transferrin saturation (r = -.62, p = .004) and (b) right frontal white matter Glx:tCr associated negatively with iron concentration (r = -.59, p = .008) and transferrin saturation (r = -.65, p = .002). Our results support associations between iron metabolism and our proxy for in vivo glutamate concentration (Glx:tCr). These associations were limited to women, suggesting a stronger regulatory control between iron and glutamate metabolism. These associations support additional fundamental research into the molecular mechanisms of this regulatory control.

The prevalence and clinical correlates of substance use disorders in patients with psychotic disorders from an Upper-Middle-Income Country.

BACKGROUND: Substance use disorders (SUDs) occur frequently in patients with psychotic disorders and have been associated with various demographic and clinical correlates. There is an absence of research on the prevalence and clinical correlates of SUDs in psychotic disorders in low-and-middle-income countries (LMICs). AIM: We aimed to determine the prevalence and correlates of SUDs in psychotic disorders. SETTING: Patients attending a large secondary-level psychiatric hospital in Cape Town South Africa. METHODS: We used the Structured Clinical Interview for DSM-IV (SCID-I) to determine psychiatric and substance use diagnoses, depressive, anxiety, obsessive-compulsive and post-traumatic symptoms. We used logistic regression models to determine significant predictors of SUDs. RESULTS: In total sample (N = 248), 55.6% of participants had any SUD, 34.3% had cannabis use disorders, 30.6% alcohol use disorders, 27.4% methamphetamine use disorders, 10.4% methaqualone use disorders and 4.8% had other SUDs. There were significant associations with male sex for most SUDs, with younger age and Coloured ethnicity for methamphetamine use disorders, and with lower educational attainment for cannabis use disorders. Anxiety symptoms and suicide attempts were significantly associated with alcohol use disorders; a diagnosis of a substance induced psychosis with cannabis and methamphetamine use disorders. Across most SUDs legal problems and criminal involvement were significantly increased. CONCLUSION: This study found a high prevalence and wide distribution of SUDs in patients with psychotic disorders, consistent with previous work from high income countries. Given clinical correlates, in individuals with psychotic disorders and SUDs it is important to assess anxiety symptoms, suicidality and criminal involvement.

Current and potential pharmacological and psychosocial interventions for anxiety symptoms and disorders in patients with schizophrenia: structured review.

OBJECTIVE: Between 30% and 62% of patients with schizophrenia present with co-morbid anxiety disorders that are associated with increased overall burden. Our aim was to summarize current and potential interventions for anxiety in schizophrenia. DESIGN: Structured review, summarizing pharmacological and psychosocial interventions used to reduce anxiety in schizophrenia and psychosis. RESULTS: Antipsychotics have been shown to reduce anxiety, increase anxiety, or have no effect. These may be augmented with another antipsychotic, anxiolytic, or antidepressant. Novel agents, such as L-theanine, pregabalin, and cycloserine, show promise in attenuating anxiety in schizophrenia. Psychosocial therapies have been developed to reduce the distress of schizophrenia. Cognitive behavioural therapy (CBT) has shown that benefit and refinements in the therapy have been successful, for example, for managing worry in schizophrenia. CBT usually involves more than 16 sessions, as short courses of CBT do not attenuate the presentation of anxiety in schizophrenia. To address time and cost, the development of manualized CBT to address anxiety in schizophrenia is being developed. CONCLUSIONS: The presence of coexisting anxiety symptoms and co-morbid anxiety disorders should be ascertained when assessing patients with schizophrenia or other psychoses as a range of pharmacological and psychosocial treatments are available.

Interhemispheric Functional Brain Connectivity in Neonates with Prenatal Alcohol Exposure: Preliminary Findings.

BACKGROUND: Children exposed to alcohol in utero demonstrate reduced white matter microstructural integrity. While early evidence suggests altered functional brain connectivity in the lateralization of motor networks in school-age children with prenatal alcohol exposure (PAE), the specific effects of alcohol exposure on the establishment of intrinsic connectivity in early infancy have not been explored. METHODS: Sixty subjects received functional imaging at 2 to 4 weeks of age for 6 to 8 minutes during quiet natural sleep. Thirteen alcohol-exposed (PAE) and 14 age-matched control (CTRL) participants with usable data were included in a multivariate model of connectivity between sensorimotor intrinsic functional connectivity networks. Seed-based analyses of group differences in interhemispheric connectivity of intrinsic motor networks were also conducted. The Dubowitz neurological assessment was performed at the imaging visit. RESULTS: Alcohol exposure was associated with significant increases in connectivity between somatosensory, motor networks, brainstem/thalamic, and striatal intrinsic networks. Reductions in interhemispheric connectivity of motor and somatosensory networks did not reach significance. CONCLUSIONS: Although results are preliminary, findings suggest PAE may disrupt the temporal coherence in blood oxygenation utilization in intrinsic networks underlying motor performance in newborn infants. Studies that employ longitudinal designs to investigate the effects of in utero alcohol exposure on the evolving resting-state networks will be key in establishing the distribution and timing of connectivity disturbances already described in older children.

Can cognitive behaviour therapy beneficially influence arousal mechanisms in psychosis?

Cognitive behavioural therapy for psychosis (CBTp) is an approved adjunct therapy for patients with psychotic disorders; however, we do not fully understand the neurobiological effects that this therapy may exert. Arousal, as measured by electroencephalography (EEG), provides a useful electrophysiological marker for assessing psychotic disorders. EEG studies may therefore serve as a useful measure for assessing the underlying effects of CBTp in psychotic disorders.

Genetic predisposition and early life experience interact to determine glutamate transporter (GLT1) and solute carrier family 12 member 5 (KCC2) levels in rat hippocampus.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common child psychiatric disorders. While it is typically treated with medications that target dopamine and norepinephrine transmission, there is increasing evidence that other neurotransmitter systems, such as glutamate and GABA, may be involved. The aetiology of ADHD is unknown; however, there is evidence that early life stress may contribute to the development of the disorder. In the present study we used proteomic analysis (iTRAQ) followed by sodium dodecyl sulfate polyacrylamide gel electrophoresis and Western blot analysis to investigate hippocampal protein profiles of three rat strains: an animal model of ADHD, spontaneously hypertensive rats (SHR), their control Wistar-Kyoto rats (WKY), and Sprague-Dawley rats (SD). We additionally investigated how these protein profiles are affected by maternal separation, a model of early life stress. Our findings show that solute carrier family 12 member 5 (KCC2) is increased in SHR hippocampus. The glutamate transporter GLT1 splice variant, GLT1b, was increased (proteomic analysis) while total GLT1 (comprised mostly of GLT1a splice variant) was reduced (Western blot analysis) in SHR hippocampus, compared to WKY and SD--a pattern that is consistent with elevated extracellular glutamate levels. Maternal separation increased total GLT1 in hippocampi of SHR, WKY, and SD, and reduced GLT1b in SHR hippocampus. Together these findings provide evidence for disturbed glutamatergic and GABAergic transmission in SHR hippocampus, maternal separation effects on glutamate uptake in hippocampi of all three strains, as well a unique effect of maternal separation on GLT1b levels in SHR hippocampus. These data suggest significant involvement of glutamatergic and GABAergic transmission in the neuropathophysiology of ADHD, and implicates changes in glutamatergic transmission as a result of early life stress.

Alcohol exposure in utero is associated with decreased gray matter volume in neonates.

Neuroimaging studies have indicated that prenatal alcohol exposure is associated with alterations in the structure of specific brain regions. However, the temporal specificity of such changes and their behavioral consequences are less known. Here we explore the brain structure of infants with in utero exposure to alcohol shortly after birth. T2 structural MRI images were acquired from 28 alcohol-exposed infants and 45 demographically matched healthy controls at 2-4 weeks of age on a 3T Siemens Allegra system as part of large birth cohort study, the Drakenstein Child Health Study (DCHS). Neonatal neurobehavior was assessed at this visit; early developmental outcome assessed on the Bayley Scales of Infant Development III at 6 months of age. Volumes of gray matter regions were estimated based on the segmentations of the University of North Carolina neonatal atlas. Significantly decreased total gray matter volume was demonstrated for the alcohol-exposed cohort compared to healthy control infants (p < 0.001). Subcortical gray matter regions that were significantly different between groups after correcting for overall gray matter volume included left hippocampus, bilateral amygdala and left thalamus (p < 0.01). These findings persisted even when correcting for infant age, gender, ethnicity and maternal smoking status. Both early neurobehavioral and developmental adverse outcomes at 6 months across multiple domains were significantly associated with regional volumes primarily in the temporal and frontal lobes in infants with prenatal alcohol exposure. Alcohol exposure during the prenatal period has potentially enduring neurobiological consequences for exposed children. These findings suggest the effects of prenatal alcohol exposure on brain growth is present very early in the first year of life, a period during which the most rapid growth and maturation occurs.

First-Rank Symptoms in Methamphetamine Psychosis and Schizophrenia.

BACKGROUND: Methamphetamine psychosis (MAP) symptomatology has been described as indistinguishable from that of schizophrenia (SZ), yet research comparing these two disorders on specific psychotic symptoms such as schneiderian first-rank symptoms (FRS) is lacking. We aimed to determine and compare the occurrence and associations of FRS in patients diagnosed with MAP and with SZ. SAMPLING AND METHOD: Data from SCID-I interviews performed on patients with either a diagnosis of SZ or MAP were compared. We calculated the prevalence of different FRS between MAP and SZ patients and used logistic regression to assess the association between FRS and diagnosis. RESULTS: 102 patients were included in the study (MAP = 33, SZ = 69). Thought broadcasting occurred significantly more often in SZ (42%) than in MAP (24.2%) patients (adjusted OR = 3.02; 95% CI: 1.12-8.15; p = 0.028), while auditory hallucinations (voices conversing) were significantly higher in MAP (48.5%) than in SZ (20.3%) patients (adjusted OR = 0.27; 95% CI: 0.10-0.66; p = 0.004). However, there was no significant difference in the occurrence of one or more FRS in MAP and SZ, with most FRS showing overlap. CONCLUSIONS: We found that first-rank auditory hallucinations were more prevalent in MAP, whereas first-rank delusions of thought broadcasting were more prevalent in SZ. However, there was a substantial overlap in MAP and SZ for most FRS. This is consistent with the finding that FRS may have limited diagnostic specificity and that there is significant overlap in the symptoms of MAP and SZ. Future research into the neurobiology of delusions and hallucinations needs to take FRS into account.

A study of the effects of prenatal alcohol exposure on white matter microstructural integrity at birth.

BACKGROUND: Neuroimaging studies have indicated that prenatal alcohol exposure is associated with alterations in the structure of specific brain regions in children. However, the temporal and regional specificity of such changes and their behavioural consequences are less known. Here we explore the integrity of regional white matter microstructure in infants with in utero exposure to alcohol, shortly after birth. METHODS: Twenty-eight alcohol-exposed and 28 healthy unexposed infants were imaged using diffusion tensor imaging sequences to evaluate white matter integrity using validated tract-based spatial statistics analysis methods. Second, diffusion values were extracted for group comparisons by regions of interest. Differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity were compared between groups and associations with measures from the Dubowitz neonatal neurobehavioural assessment were examined. RESULTS: Lower AD values (p<0.05) were observed in alcohol-exposed infants in the right superior longitudinal fasciculus compared with non-exposed infants. Altered FA and MD values in alcohol-exposed neonates in the right inferior cerebellar were associated with abnormal neonatal neurobehaviour. CONCLUSION: These exploratory data suggest that prenatal alcohol exposure is associated with reduced white matter microstructural integrity even early in the neonatal period. The association with clinical measures reinforces the likely clinical significance of this finding. The location of the findings is remarkably consistent with previously reported studies of white matter structural deficits in older children with a diagnosis of foetal alcohol spectrum disorders.

Structural brain changes in prenatal methamphetamine-exposed children.

The global use of methamphetamine (MA) has increased substantially in recent years, but the effect of MA on brain structure in prenatally exposed children is understudied. Here we aimed to investigate potential changes in brain volumes and cortical thickness of children with prenatal MA-exposure compared to unexposed controls. Eighteen 6-year old children with MA-exposure during pregnancy and 18 healthy controls matched for age, gender and socio-economic background underwent structural imaging. Brain volumes and cortical thickness were assessed using Freesurfer and compared using ANOVA. Left putamen volume was significantly increased, and reduced cortical thickness was observed in the left hemisphere of the inferior parietal, parsopercularis and precuneus areas of MA-exposed children compared to controls. Compared to control males, prenatal MA-exposed males had greater volumes in striatal and associated areas, whereas MA-exposed females predominantly had greater cortical thickness compared to control females. In utero exposure to MA results in changes in the striatum of the developing child. In addition, changes within the striatal, frontal, and parietal areas are in part gender dependent.

Mindfulness based cognitive therapy may improve emotional processing in bipolar disorder: pilot ERP and HRV study.

Emotional processing in bipolar disorder (BD) is impaired. We aimed to measure the effects of mindfulness based cognitive-behavioral therapy (MBCT) in BD on emotional processing, as measured by event related potentials (ERP) and by heart rate variability (HRV). ERP and HRV were recorded during the completion of a visual matching task, which included object matching, affect matching, and affect labeling. Individuals with BD (n = 12) were compared with controls (n = 9) to obtain baseline data prior to the individuals with BD undergoing an 8-week MBCT intervention. ERP and HRV recording was repeated after the MBCT intervention in BD. Participants with BD had exaggerated ERP N170 amplitude and increased HRV HF peak compared to controls, particularly during the affect matching condition. After an 8-week MBCT intervention, participants with BD showed attenuation of ERP N170 amplitude and reduced HRV HF peak. Our findings support findings from the literature emphasizing that emotional processing in BD is altered, and suggesting that MBCT may improve emotional processing in BD.

Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).