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.

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.

Obesity and brain structure in schizophrenia - ENIGMA study in 3021 individuals.

Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.

Weight and metabolic changes in early psychosis-association with daily quantification of medication exposure during the first hospitalization.

BACKGROUND: The most common causes of death in schizophrenia are cardiovascular disorders, which are closely related to metabolic syndrome/obesity. To better understand the development of metabolic alterations early in the course of illness, we quantified daily medication exposure in the first days of the first hospitalization for psychosis and related it to changes in weight and metabolic markers. STUDY DESIGN: We recruited participants with first episode psychosis (FEP, N = 173) during their first psychiatric hospitalization and compared them to controls (N = 204). We prospectively collected weight, body mass index, metabolic markers, and exact daily medication exposure at admission and during hospitalization. STUDY RESULTS: Individuals with FEP gained on average 0.97 ± 2.26 BMI points or 3.46 ± 7.81 kg of weight after an average of 44.6 days of their first inpatient treatment. Greater antipsychotic exposure was associated with greater BMI increase, but only in people with normal/low baseline BMI. Additional predictors of weight gain included type of medication and duration of treatment. Medication exposure was not directly related to metabolic markers, but higher BMI was associated with higher TGC, TSH, and lower HDL. Following inpatient treatment, participants with FEP had significantly higher BMI, TGC, prolactin, and lower fT4, HDL than controls. CONCLUSION: During their first admission, people with FEP, especially those with normal/low baseline BMI, showed a rapid and clinically significant weight increase, which was associated with exposure to antipsychotics, and with metabolic changes consistent with metabolic syndrome. These findings emphasize weight monitoring in FEP and suggest a greater need for caution when prescribing metabolically problematic antipsychotics to people with lower BMI.

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.

Demographic and clinical characteristics of lithium-treated older adults with bipolar disorder.

OBJECTIVES: There is limited information on the characteristics of older adults with bipolar disorder (OABD) treated with lithium, along with safety concerns about its use by older adults. The aim of the present study is to describe the demographic and clinical characteristics of OABD receiving lithium therapy, using data from the Global Ageing & Geriatric Experiments in Bipolar Disorder (GAGE-BD). EXPERIMENTAL PROCEDURES: Cross-sectional analysis of the GAGE-BD dataset to determine differences and similarities between lithium users and non-users. We analysed data from 986 participants aged 50 years or older (mean age 63.5 years; 57.5% females) from 12 study sites. Two subgroups ('Lithium'; 'Non-lithium') were defined according to the current prescription of lithium. We compared several outcomes between these groups, controlling for age, gender, and study site. RESULTS: OABD treated with lithium had lower scores on depression rating scales and were less likely to be categorised as with moderate or severe depression. There was a lower proportion of lithium users than non-users among those with evidence of rapid cycling and non-bipolar psychiatric diagnoses. Assessment of global cognitive state and functionality indicated better performance among lithium users. The current use of antipsychotics was less frequent among lithium users, who also reported fewer cardiovascular comorbidities than non-users. CONCLUSION: We found several potentially relevant differences in the clinical profile of OABD treated with lithium compared with those treated with other mood stabilisers. However, the interpretation of the present results must take into account the methodological limitations inherent to the cross-sectional approach and data harmonisation.

Experience modulates motor imagery-based brain activity.

Whether or not brain activation during motor imagery (MI), the mental rehearsal of movement, is modulated by experience (i.e. skilled performance, achieved through long-term practice) remains unclear. Specifically, MI is generally associated with diffuse activation patterns that closely resemble novice physical performance, which may be attributable to a lack of experience with the task being imagined vs. being a distinguishing feature of MI. We sought to examine how experience modulates brain activity driven via MI, implementing a within- and between-group design to manipulate experience across tasks as well as expertise of the participants. Two groups of 'experts' (basketball/volleyball athletes) and 'novices' (recreational controls) underwent magnetoencephalography (MEG) while performing MI of four multi-articular tasks, selected to ensure that the degree of experience that participants had with each task varied. Source-level analysis was applied to MEG data and linear mixed effects modelling was conducted to examine task-related changes in activity. Within- and between-group comparisons were completed post hoc and difference maps were plotted. Brain activation patterns observed during MI of tasks for which participants had a low degree of experience were more widespread and bilateral (i.e. within-groups), with limited differences observed during MI of tasks for which participants had similar experience (i.e. between-groups). Thus, we show that brain activity during MI is modulated by experience; specifically, that novice performance is associated with the additional recruitment of regions across both hemispheres. Future investigations of the neural correlates of MI should consider prior experience when selecting the task to be performed.

Asymmetric Weighting to Optimize Regional Sensitivity in Combined fMRI-MEG Maps.

Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) are neuroimaging techniques that measure inherently different physiological processes, resulting in complementary estimates of brain activity in different regions. Combining the maps generated by each technique could thus provide a richer understanding of brain activation. However, present approaches to integration rely on a priori assumptions, such as expected patterns of brain activation in a task, or use fMRI to bias localization of MEG sources, diminishing fMRI-invisible sources. We aimed to optimize sensitivity to neural activity by developing a novel method of integrating data from the two imaging techniques. We present a data-driven method of integration that weights fMRI and MEG imaging data by estimates of data quality for each technique and region. This method was applied to a verbal object recognition task. As predicted, the two imaging techniques demonstrated sensitivity to activation in different regions. Activity was seen using fMRI, but not MEG, throughout the medial temporal lobes. Conversely, activation was seen using MEG, but not fMRI, in more lateral and anterior temporal lobe regions. Both imaging techniques were sensitive to activation in the inferior frontal gyrus. Importantly, integration maps retained activation from individual activation maps, and showed an increase in the extent of activation, owing to greater sensitivity of the integration map than either fMRI or MEG alone.

Trajectories of daily antipsychotic use and weight gain in people hospitalized for the first episode of psychosis.

BACKGROUND: We need to better understand the risk factors and predictors of medication-related weight gain to improve metabolic health of individuals with schizophrenia. This study explores how trajectories of antipsychotic medication (AP) use impact body weight early in the course of schizophrenia. METHODS: We recruited 92 participants with first-episode psychosis (FEP, n = 92) during their first psychiatric hospitalization. We prospectively collected weight, body mass index (BMI), metabolic markers, and exact daily medication exposure during 6-week hospitalization. We quantified the trajectory of AP medication changes and AP polypharmacy using a novel approach based on meta-analytical ranking of medications and tested it as a predictor of weight gain together with traditional risk factors. RESULTS: Most people started treatment with risperidone (n = 57), followed by olanzapine (n = 29). Then, 48% of individuals remained on their first prescribed medication, while 33% of people remained on monotherapy. Almost half of the individuals (39/92) experienced escalation of medications, mostly switch to AP polypharmacy (90%). Only baseline BMI was a predictor of BMI change. Individuals in the top tercile of weight gain, compared to those in the bottom tercile, showed lower follow-up symptoms, a trend for longer prehospitalization antipsychotic treatment, and greater exposure to metabolically problematic medications. CONCLUSIONS: Early in the course of illness, during inpatient treatment, baseline BMI is the strongest and earliest predictor of weight gain on APs and is a better predictor than type of medication, polypharmacy, or medication switches. Baseline BMI predicted weight change over a period of weeks, when other traditional predictors demonstrated a much smaller effect.

Spatial MEG laterality maps for language: clinical applications in epilepsy.

Functional imaging is increasingly being used to provide a noninvasive alternative to intracarotid sodium amobarbitol testing (i.e., the Wada test). Although magnetoencephalography (MEG) has shown significant potential in this regard, the resultant output is often reduced to a simplified estimate of laterality. Such estimates belie the richness of functional imaging data and consequently limit the potential value. We present a novel approach that utilizes MEG data to compute "complex laterality vectors" and consequently "laterality maps" for a given function. Language function was examined in healthy controls and in people with epilepsy. When compared with traditional laterality index (LI) approaches, the resultant maps provided critical information about the magnitude and spatial characteristics of lateralized function. Specifically, it was possible to more clearly define low LI scores resulting from strong bilateral activation, high LI scores resulting from weak unilateral activation, and most importantly, the spatial distribution of lateralized activation. We argue that the laterality concept is better presented with the inherent spatial sensitivity of activation maps, rather than being collapsed into a one-dimensional index.

Consequences of Contracting COVID-19 or Taking the COVID-19 Vaccine for Individuals with a History of Lyme Disease.

Individuals with Lyme disease can be very symptomatic. This survey compares the burden of illness for individuals with a history of Lyme disease (HLD) with individuals with a HLD who have either contracted COVID-19 or who have taken the COVID-19 vaccine. The findings describe the relative symptom burden among these three groups using a cross-sectional descriptive survey investigating the burden of Lyme disease in a pandemic. The survey includes the General Symptom Questionnaire-30 (GSQ-30), a brief self-report scale designed to assess the symptom burden in Lyme disease (LD). The results of this survey show that the overall burden of illness among individuals with HLD is not significantly different after contracting COVID-19 or after COVID-19 vaccination. A new survey will be needed to better understand why one in five individuals with a HLD reported long COVID after contracting COVID-19. These results should help clinicians and their patients to discuss the consequences of contracting a COVID-19 infection or being vaccinated against COVID-19.

Extended and replicated white matter changes in obesity: Voxel-based and region of interest meta-analyses of diffusion tensor imaging studies.

Introduction: Obesity has become a global public health issue, which impacts general health and the brain. Associations between obesity and white matter microstructure measured using diffusion tensor imaging have been under reviewed, despite a relatively large number of individual studies. Our objective was to determine the association between obesity and white matter microstructure in a large general population sample. Methods: We analyzed location of brain white matter changes in obesity using the Anisotropic Effect Size Seed-based d Mapping (AES-SDM) method in a voxel-based meta-analysis, with validation in a region of interest (ROI) effect size meta-analysis. Our sample included 21 742 individuals from 51 studies. Results: The voxel-based spatial meta-analysis demonstrated reduced fractional anisotropy (FA) with obesity in the genu and splenium of the corpus callosum, middle cerebellar peduncles, anterior thalamic radiation, cortico-spinal projections, and cerebellum. The ROI effect size meta-analysis replicated associations between obesity and lower FA in the genu and splenium of the corpus callosum, middle cerebellar peduncles. Effect size of obesity related brain changes was small to medium. Discussion: Our findings demonstrate obesity related brain white matter changes are localized rather than diffuse. Better understanding the brain correlates of obesity could help identify risk factors, and targets for prevention or treatment of brain changes.

Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group.

BACKGROUND: Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD. METHODS: Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables. RESULTS: Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18

Using CForest to Analyze Diffusion Tensor Imaging Data: A Study of White Matter Integrity in Healthy Aging.

Healthy aging has been associated with a global reduction in white matter integrity, which is thought to reflect cognitive decline. The present study aimed to investigate this reduction over a broad range of the life span, using diffusion tensor imaging analyzed with conditional inference random forest modeling (CForest). This approach is sensitive to subtle and potentially nonlinear effects over the age continuum and was used to characterize the progression of decline in greater detail than has been possible in the past. Data were collected from 45 healthy individuals ranging in age from 19 to 67 years. Fractional anisotropy (FA) was estimated using probabilistic tractography for a number of major tracts across the brain. Age coincided with a nonlinear decrease in FA, with onset beginning at ∼30 years of age and the steepest declines occurring later in life. However, several tracts showed a transient increase before this decline. The progression of decline varied by tract, with steeper but later decline occurring in more anterior tracts. Finally, strongly right-handed individuals demonstrated relatively preserved FA until more than a decade following the onset of decline of others. These results demonstrate that using a novel, nonparametric analysis approach, previously reported reductions in FA with healthy aging were confirmed, while at the same time, new insight was provided into the onset and progression of decline, with evidence suggesting increases in integrity continuing into adulthood.

Comparing gray and white matter fMRI activation using asymmetric spin echo spiral.

Recent developments have shown that it is possible to detect functional magnetic resonance imaging (fMRI) activation in white matter (WM). Enhanced sensitivity to WM fMRI signals has been associated with the asymmetric spin echo (ASE) spiral sequence. The ASE spiral sequence produces three consecutive images that have equal blood-oxygen level-dependent (BOLD) contrast but increasing T(2) contrast. The current study evaluated whether ASE spiral sensitivity differed between white and gray matter in the corpus callosum, superior parietal lobes, cingulate gyrus, and inferior frontal lobes. Contrast and noise were compared across the three images for each region. Results showed increasing gains in functional contrast in both white and gray matter as a function of T(2) contrast. The third image, with the most T(2) contrast, showed the largest increase in contrast, while changes in noise were maintained. The results suggest that ASE spiral increases fMRI sensitivity globally through the addition of T(2) weighted contrast.