MR imaging of the brain in large cohort studies: feasibility report of the population- and patient-based BiDirect study

Cerebral MRI in a prospective cohort study on depression and atherosclerosis: the BiDirect sample, processing pipelines, and analysis tools

BACKGROUND

The use of cerebral magnetic resonance imaging (MRI) in observational studies has increased exponentially in recent years, making it critical to provide details about the study sample, image processing, and extracted imaging markers to validate and replicate study results. This article reviews the cerebral MRI dataset from the now-completed BiDirect cohort study, as an update and extension of the feasibility report published after the first two examination time points.

METHODS

We report the sample and flow of participants spanning four study sessions and twelve years. In addition, we provide details on the acquisition protocol; the processing pipelines, including standardization and quality control methods; and the analytical tools used and markers available.

RESULTS

All data were collected from 2010 to 2021 at a single site in Münster, Germany, starting with a population of 2,257 participants at baseline in 3 different cohorts: a population-based cohort (n = 911 at baseline, 672 with MRI data), patients diagnosed with depression (n = 999, 736 with MRI data), and patients with manifest cardiovascular disease (n = 347, 52 with MRI data). During the study period, a total of 4,315 MRI sessions were performed, and over 535 participants underwent MRI at all 4 time points.

CONCLUSIONS

Images were converted to Brain Imaging Data Structure (a standard for organizing and describing neuroimaging data) and analyzed using common tools, such as CAT12, FSL, Freesurfer, and BIANCA to extract imaging biomarkers. The BiDirect study comprises a thoroughly phenotyped study population with structural and functional MRI data.

RELEVANCE STATEMENT

The BiDirect Study includes a population-based sample and two patient-based samples whose MRI data can help answer numerous neuropsychiatric and cardiovascular research questions.

KEY POINTS

• The BiDirect study included characterized patient- and population-based cohorts with MRI data. • Data were standardized to Brain Imaging Data Structure and processed with commonly available software. • MRI data and markers are available upon request.

The R package for DICOM to brain imaging data structure conversion

The BIDSconvertR package is the first R-based tool for organizing magnetic resonance imaging (MRI) research data in accordance with the Brain Imaging Data Structure (BIDS) specification. Key features are the DICOM (Digital Imaging and Communications in Medicine) to NIfTI (Neuroimaging Informatics Technology Initiative) and NIfTI to BIDS conversion, the implementation of the BIDS Validator and a MRI data viewer to efficiently manage MRI neuroimaging data sets. The BIDSconvertR offers an interactive user dialogue and a graphical user interface. BIDS validation is facilitated by color-coding of the BIDS sequence-IDs. Data cleaning is simplified by the option of using regular expressions. The BIDSconvertR contributes to the growing efforts to improve reproducibility in neuroimaging research by facilitating researchers to share and organize data in a standardized and transparent manner.

Oncological and functional neurosurgery: Perspectives for the decade regarding diffuse gliomas

For decades, diffuse glioma (DG) studies mostly focused on oncological considerations, whereas functional outcomes received less attention. Currently, because overall survival has increased in DG, especially in low-grade glioma (overall survival > 15 years), quality of life including neurocognitive and behavioral aspects should be assessed and preserved more systematically, particularly regarding surgery. Indeed, early maximal tumor removal results in greater survival in both high-grade and low-grade gliomas, leading to propose "supra-marginal" resection, with excision of the peritumoral zone in diffuse neoplasms. To minimize functional risks while maximizing the extent of resection, traditional "tumor-mass resection" is replaced by "connectome-guided resection" conducted under awake mapping, taking into account inter-individual brain anatomo-functional variability. A better understanding of the dynamic interplay between DG progression and reactional neuroplastic mechanisms is critical to adapt a personalized multistage therapeutic strategy, with integration of functional neurooncological (re)operation(s) in a multimodal management scheme including repeated medical therapies. Because the therapeutic armamentarium remains limited, the aims of this paradigmatic shift are to predict one/several step(s) ahead glioma behavior, its modifications, and compensatory neural networks reconfiguration over time in order to optimize the onco-functional benefit of each treatment - either in isolation or in combination with others - in human beings bearing a chronic tumoral disease while enjoying an active familial and socio-professional life as close as possible to their expectations. Thus, new ecological endpoints such as return to work should be incorporated into future DG trials. "Preventive neurooncology" might also be envisioned, by proposing a screening policy to discover and treat incidental glioma earlier.

Periodic limb movements in sleep are linked to decreased hippocampus and amygdala volumes in the population-based BiDirect Study

STUDY OBJECTIVES

Even though numerous studies indicate that sleep disorders are associated with altered brain morphology, MRI studies focusing on periodic limb movements in sleep (PLMS) are scarce. Our aim was to investigate the association of PLMS with global and regional gray matter volumes as well as white matter hyperintensity (WMH) volume.

METHODS

One hundred and eighty-nine subjects (57.0 ± 7.8 years, women: 50.5%) of the population-based BiDirect Study underwent a single-night polysomnography (PSG). Standard criteria of the American Academy of Sleep Medicine were applied to evaluate sleep characteristics and calculate the PLMS index (PLMSI). T1w and FLAIR images were acquired with cerebral MRI at 3 Tesla. Voxel-based morphometry was performed to determine the total gray matter volume as well as the volume of cortical segments and subcortical gray matter areas using SPM12 and CAT12. The WMH volume was quantified with the Brain Intensity AbNormality Classification Algorithm. The independent relationship between MRI markers and PLMSI was analyzed using multivariable linear regression with adjustment for age, sex, body mass index, intracranial volume, PSG scorer, PSG device, sleep apnea, and the use of antidepressants.

RESULTS

PLMSI was not significantly related to global gray matter volume and WMH volume. However, significant inverse associations of the PLMSI with the volume of the hippocampus (left and right hemisphere) and left amygdala were observed.

CONCLUSIONS

A significant relationship between a higher PLMSI and lower volumes of the hippocampus and amygdala was found among the participants of the BiDirect Study. Since these associations are based on exploratory analyses, further replications are required before drawing firm conclusions.

Sex-Specific Causes and Consequences of White Matter Damage in a Middle-Aged Cohort

Objective

To evaluate potential sex-specific effects of multiple cardiovascular risk factors on white matter pathology in normal aging men and women, as well as potential sex-differences in the association of white matter pathology and cognitive functions.

Methods

We analyzed cross-sectional data of 581 participants (median age: 53 years, 54% women) of the population-based cohort of the BiDirect Study who completed clinical examinations, five neuropsychological tests, and an 3T MRI examination. White matter pathology was determined by the extent of white matter hyperintensities (WMH) on FLAIR images as well as the magnitude of global fractional anisotropy (FA) based on diffusion tensor imaging. Main effects, interaction as well as sex-stratified generalized linear regression models were used to evaluate the moderating effect of sex on the association of hypertension, diabetes mellitus, smoking, and obesity with WMH and FA, respectively. Associations of imaging markers with cognitive test results were determined with linear regression models.

Results

Hypertension showed stronger associations with more extensive WMH and less FA in women compared to men. Current smoking was associated with more severe WMH in women only. Adjusted for age and education, WMH were not significantly associated with cognitive tests, but higher FA was associated with better performance in motor function in both sexes and with executive functions in men, even after adjustment for cardiovascular risk factors.

Conclusion

We observed a stronger association of hypertension and smoking with white matter damage in women, suggesting a higher susceptibility for vascular pathology in women. However, there was no association of WMH with cognition, and FA was associated with executive function tests only in men, suggesting a higher cognitive reserve in women.

The Relative Importance of Vascular Risk Factors on Early Cognitive Aging Varies Only Slightly Between Men and Women

Objective

To investigate the sex-specific course and impact of vascular risk factors on cognitive aging in a rather young and healthy community-dwelling cohort.

Methods

We used data from a population-based cohort study, collected three times during 6 years, comprising 1,911 examinations from 798 participants aged 35–66 years at baseline. Cognitive performance on the Color-Word-Interference-Test, the Trail Making Tests (TMT) A&B, the Word Fluency Test, a 12-item word list, the Purdue Pegboard Test and a principal component global score were used as outcomes in linear mixed models. We evaluated (1) sex differences in cognitive trajectories, (2) the mediating role of hypertension, diabetes, smoking and obesity [body mass index (BMI) > 30] on sex differences and (3) in sex-stratified analyses, potential sex-specific effects of these risk factors on cognition.

Results

For all cognitive tests, we observed cognitive decline with age. Rates of decline slightly differed across sexes, showing a later but steeper decline for women in tests of memory (word list) and word fluency, but a steeper decline for men in tests of psychomotor speed and mental set shifting (TMT A&B) in older age. Women generally scored better on cognitive tests, but the slightly higher prevalence of classical vascular risks factors in men in our cohort could not explain these sex differences. Sex-stratified analyses revealed a generally small, concordantly negative, but quantitatively slightly different impact of diabetes, smoking and obesity on cognitive functions but mixed effects for arterial hypertension, depending on the blood pressure values, the treatment status and the duration of arterial hypertension.

Conclusion

Cognitive sex differences in this rather young and healthy cohort could not be explained by a differing prevalence of vascular risks factors across sexes. The association of cardiovascular risk factors with cognition, however, slightly differed between men and women, whereby effects were generally small. Whereas longtime diabetes, obesity and smoking had a sex-specific, but concordantly negative impact on psychomotor speed, executive and motor functions, we found some opposing effects for arterial hypertension. Our results can help to identify sex-specific susceptibilities to modifiable risk factors, to attract attention to potential information bias and to stimulate further research into alternative causes and mechanism of sex differences in cognitive aging.

The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.

Methods: We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (n = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort.

Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes.

Discussion: We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.

Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium

BACKGROUND

Neuroimaging studies have consistently reported similar brain structural abnormalities across different psychiatric disorders. Yet, the extent and regional distribution of shared morphometric abnormalities between disorders remains unknown.

METHODS

Here, we conducted a cross-disorder analysis of brain structural abnormalities in 6 psychiatric disorders based on effect size estimates for cortical thickness and subcortical volume differences between healthy control subjects and psychiatric patients from 11 mega- and meta-analyses from the ENIGMA (Enhancing Neuro Imaging Genetics Through Meta Analysis) consortium. Correlational and exploratory factor analyses were used to quantify the relative overlap in brain structural effect sizes between disorders and to identify brain regions with disorder-specific abnormalities.

RESULTS

Brain structural abnormalities in major depressive disorder, bipolar disorder, schizophrenia, and obsessive-compulsive disorder were highly correlated (r = .443 to r = .782), and one shared latent underlying factor explained between 42.3% and 88.7% of the brain structural variance of each disorder. The observed shared morphometric signature of these disorders showed little similarity with brain structural patterns related to physiological aging. In contrast, patterns of brain structural abnormalities independent of all other disorders were observed in both attention-deficit/hyperactivity disorder and autism spectrum disorder. Brain regions showing high proportions of independent variance were identified for each disorder to locate disorder-specific morphometric abnormalities.

CONCLUSIONS

Taken together, these results offer novel insights into transdiagnostic as well as disorder-specific brain structural abnormalities across 6 major psychiatric disorders. Limitations comprise the uncertain contribution of risk factors, comorbidities, and medication effects to the observed pattern of results that should be clarified by future research.

Biological sex classification with structural MRI data shows increased misclassification in transgender women

Transgender individuals (TIs) show brain-structural alterations that differ from their biological sex as well as their perceived gender. To substantiate evidence that the brain structure of TIs differs from male and female, we use a combined multivariate and univariate approach. Gray matter segments resulting from voxel-based morphometry preprocessing of N = 1753 cisgender (CG) healthy participants were used to train (N = 1402) and validate (20% holdout N = 351) a support-vector machine classifying the biological sex. As a second validation, we classified N = 1104 patients with depression. A third validation was performed using the matched CG sample of the transgender women (TW) application sample. Subsequently, the classifier was applied to N = 26 TW. Finally, we compared brain volumes of CG-men, women, and TW-pre/post treatment cross-sex hormone treatment (CHT) in a univariate analysis controlling for sexual orientation, age, and total brain volume. The application of our biological sex classifier to the transgender sample resulted in a significantly lower true positive rate (TPR-male = 56.0%). The TPR did not differ between CG-individuals with (TPR-male = 86.9%) and without depression (TPR-male = 88.5%). The univariate analysis of the transgender application-sample revealed that TW-pre/post treatment show brain-structural differences from CG-women and CG-men in the putamen and insula, as well as the whole-brain analysis. Our results support the hypothesis that brain structure in TW differs from brain structure of their biological sex (male) as well as their perceived gender (female). This finding substantiates evidence that TIs show specific brain-structural alterations leading to a different pattern of brain structure than CG-individuals.

Diagnostic Value of Diffusion Tensor Imaging and Positron Emission Tomography in Early Stages of Frontotemporal Dementia

BACKGROUND

Due to suboptimal sensitivity and specificity of structural and molecular neuroimaging tools, the diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging.

OBJECTIVE

Investigation of the sensitivity of diffusion tensor imaging (DTI) and fluorodeoxyglucose positron emission tomography (FDG-PET) to detect cerebral alterations in early stages of bvFTD despite inconspicuous conventional MRI.

METHODS

Thirty patients with early stages of bvFTD underwent a detailed neuropsychological examination, cerebral 3T MRI with DTI analysis, and FDG-PET. After 12 months of follow-up, all patients finally fulfilled the diagnosis of bvFTD. Individual FDG-PET data analyses showed that 20 patients exhibited a "typical" pattern for bvFTD with bifrontal and/or temporal hypometabolism (bvFTD/PET+), and that 10 patients showed a "non-typical"/normal pattern (bvFTD/PET-). DTI data were compared with 42 healthy controls in an individual and voxel-based group analysis. To examine the clinical relevance of the findings, associations between pathologically altered voxels of DTI or FDG-PET results and behavioral symptoms were estimated by linear regression analyses.

RESULTS

DTI voxel-based group analyses revealed microstructural degeneration in bifrontal and bitemporal areas in bvFTD/PET+ and bvFTD/PET- groups. However, when comparing the sensitivity of individual DTI data analysis with FDG-PET, DTI appeared to be less sensitive. Neuropsychological symptoms were considerably related to neurodegeneration within frontotemporal areas identified by DTI and FDG-PET.

CONCLUSION

DTI seems to be an interesting tool for detection of functionally relevant neurodegenerative alterations in early stages of bvFTD, even in bvFTD/PET- patients. However, at a single subject level, it seems to be less sensitive than FDG-PET. Thus, improvement of individual DTI analysis is necessary.

How to report incidental findings from population whole-body MRI: view of participants of the German National Cohort

OBJECTIVES

In the German National Cohort (GNC), 30,000 individuals are examined with whole-body MRI (wbMRI), of which about 3000 participants are expected to receive an incidental finding (IF) disclosure. In order to get feedback from participants and to evaluate the IF-management procedure of the wbMRI substudy, a follow-up questionnaire was developed. This single-center pilot trial was aimed to get a first impression on feasibility reproducibility and validity of such a survey in order to take necessary adjustments before initiating the survey among several thousand participants.

METHODS

The questionnaires were sent out in test-retest manner to 86 participants who received a wbMRI examination in January-February 2016 at the imaging center in Neubrandenburg. The ratio of participants with and without IF notification was 1:1. Descriptive statistics was performed.

RESULTS

A first response of 94% and completion proportion of 99% were achieved. Participants were satisfied with the examination procedure. Ninety-five percent of participants considered it very important to receive notification of IFs. Participants reported minimal stress levels while waiting for a possible IF notification letter, but high stress levels when an IF letter was received. Phrasing of the IF reports was rated in 97% as well understandable and in 55% as beneficial to health status.

CONCLUSIONS

This questionnaire will serve researchers within the GNC as a fundamental instrument not only for quality management analyses but also for the investigation of still unacknowledged scientific and ethical questions contributing to evidence-based guidelines concerning the complex approach to IFs in future population-based imaging.

KEY POINTS

• Evidence-based guidelines for reporting incidental findings in population whole-body MRI are lacking. • Pilot-testing of a questionnaire for the evaluation of practical and ethical aspects of the procedure to report incidental findings in the German National Cohort shows a high level of acceptance and high return rate by participants. • Participants reported minimal stress levels while waiting for a possible incidental finding notification letter, which increased significantly, when such a letter was received.

Gray matter correlates of pressure pain thresholds and self-rated pain sensitivity: a voxel-based morphometry study

Individual differences in sensitivity to pain are large and have clinical and scientific importance. Although heavily influenced by situational factors, they also relate to genetic factors and psychological traits, and are reflected by differences in functional activation in pain-related brain regions. Here, we used voxel-based morphometry to investigate if individual pain sensitivity is related to local gray matter volumes. Pain sensitivity was determined using (1) index finger pressure pain thresholds (PPTs) and (2) pain intensity ratings of imagined painful situations as assessed by the Pain Sensitivity Questionnaire (PSQ) in 501 population-based subjects participating in the BiDirect Study. Pain Sensitivity Questionnaire scores were positively associated with gray matter in 2 symmetrical clusters, with a focus on the parahippocampal gyrus, extending to the hippocampus, fusiform gyrus, BA19, putamen, and insula (P < 0.05 corrected), but the effect was small (R = 0.045-0.039). No negative associations with the PSQ and no associations with the PPT reached significance. Parahippocampal activation during pain and altered parahippocampal gray matter in chronic pain have been reported, which would be consistent with positive associations with PSQ scores. Alternatively, associations of PSQ scores with the parahippocampal and fusiform gray matter could relate to the visual imagination of painful situations required by the PSQ, not to pain sensitivity itself. Regarding PPTs, the present data obtained in a large sample strongly suggest an absence of associations of this parameter with gray matter volume. In conclusion, the present results argue against a strong association between pain sensitivity and local gray matter volumes.

Interaction of Developmental Venous Anomalies with Resting-State Functional MRI Measures

BACKGROUND AND PURPOSE

Functional MR imaging of the brain, used for both clinical and neuroscientific applications, relies on measuring fluctuations in blood oxygenation. Such measurements are susceptible to noise of vascular origin. The purpose of this study was to assess whether developmental venous anomalies, which are frequently observed normal variants, can bias fMRI measures by appearing as true neural signal.

MATERIALS AND METHODS

Large developmental venous anomalies (1 in each of 14 participants) were identified from a large neuroimaging cohort (n = 814). Resting-state fMRI data were decomposed using independent component analysis, a data-driven technique that creates distinct component maps representing aspects of either structured noise or true neural activity. We searched all independent components for maps that exhibited a spatial distribution of their signals following the topography of developmental venous anomalies.

RESULTS

Of the 14 developmental venous anomalies identified, 10 were clearly present in 17 fMRI independent components in total. While 9 (52.9%) of these 17 independent components were dominated by venous contributions and 2 (11.8%) by motion artifacts, 2 independent components (11.8%) showed partial neural signal contributions and 5 independent components (29.4%) unambiguously exhibited typical neural signal patterns.

CONCLUSIONS

Developmental venous anomalies can strongly resemble neural signal as measured by fMRI. They are thus a potential source of bias in fMRI analyses, especially when present in the cortex. This could impede interpretation of local activity in patients, such as in presurgical mapping. In scientific studies with large samples, developmental venous anomaly confounds could be mainly addressed using independent component analysis-based denoising.

Higher-Order Surgical Questions for Diffuse Low-Grade Gliomas: Supramaximal Resection, Neuroplasticity, and Screening

Diffuse low-grade glioma (DLGG) is a brain neoplasm that migrates within the connectome and that becomes malignant if left untreated. Early and maximal safe surgical resection by means of awake mapping enables a significant improvement of survival and quality of life. Supramaximal functional-based resection seems to prevent DLGG malignant transformation. Neuroplasticity is helpful to remove DLGG in eloquent areas. When radical excision cannot be achieved due to invasion of critical neural networks, cerebral remapping over time may lead to a reoperation with an optimized resection. To discover and treat DLGG earlier, a screening in the general population should be considered.

Lack of Association Between Shape and Volume of Subcortical Brain Structures and Restless Legs Syndrome

Objective

Previous studies on patients with restless legs syndrome (RLS) yielded inconclusive results in the magnetic resonance imaging (MRI)-based analyses of alterations of subcortical structures in the brain. The aim of this study was to compare volumes as well as shapes of subcortical structures and the hippocampus between RLS cases and controls. Additionally, the associations between the genetic risks for RLS and subcortical volumes were investigated.

Methods

We compared volumetric as well as shape differences assessed by 3 T MRI in the caudate nucleus, hippocampus, globus pallidus, putamen, and thalamus in 39 RLS cases versus 117 controls, nested within a population-based sample. In a subsample, we explored associations between known genetic risk markers for RLS and the volumes of the subcortical structures and the hippocampus.

Results

No significant differences between RLS cases and controls in subcortical and hippocampal shapes and volumes were observed. Furthermore, the genetic risk for RLS was unrelated to any alterations of subcortical and hippocampal gray matter volume.

Interpretation

We conclude that neither RLS nor the genetic risk for the disease give rise to changes in hippocampal and subcortical shapes and gray matter volumes.

Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects

OBJECTIVES

Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample. The secondary objective was to assess, if such information can improve diagnostic classification.

MATERIALS AND METHODS

We analyzed data from 360 patients with unipolar depression and 360 non-depressed population controls, who were subdivided into two independent subsets. Cluster analyses (unsupervised learning) of functional connectivity were used to generate hypotheses about potential patient subgroups from the first subset. The relationship of clusters with demographical and clinical measures was assessed. Subsequently, diagnostic classifiers (supervised learning), which incorporated information about these putative depression subgroups, were trained.

RESULTS

Exploratory cluster analyses revealed two weakly separable subgroups of depressed patients. These subgroups differed in the average duration of depression and in the proportion of patients with concurrently severe depression and anxiety symptoms. The diagnostic classification models performed at chance level.

LIMITATIONS

It remains unresolved, if subgroups represent distinct biological subtypes, variability of continuous clinical variables or in part an overfitting of sparsely structured data.

CONCLUSIONS

Functional connectivity in unipolar depression is associated with general disease effects. Cluster analyses provide hypotheses about potential depression subtypes. Diagnostic models did not benefit from this additional information regarding heterogeneity.

Prefrontal gray matter volume mediates genetic risks for obesity

Genetic and neuroimaging research has identified neurobiological correlates of obesity. However, evidence for an integrated model of genetic risk and brain structural alterations in the pathophysiology of obesity is still absent. Here we investigated the relationship between polygenic risk for obesity, gray matter structure and body mass index (BMI) by the use of univariate and multivariate analyses in two large, independent cohorts (n=330 and n=347). Higher BMI and higher polygenic risk for obesity were significantly associated with medial prefrontal gray matter decrease, and prefrontal gray matter was further shown to significantly mediate the effect of polygenic risk for obesity on BMI in both samples. Building on this, the successful individualized prediction of BMI by means of multivariate pattern classification algorithms trained on whole-brain imaging data and external validations in the second cohort points to potential clinical applications of this imaging trait marker.

Reduced fractional anisotropy in patients with major depressive disorder and associations with vascular stiffness

Previous studies revealed several alterations of the cerebral white matter in patients with major depressive disorder. However, it is unknown if these alterations are associated with vascular changes in the brain and other body parts. We compared diffusion tensor imaging derived fractional anisotropy in a well characterized sample of middle-aged patients with major depressive disorder (n = 290) and never-depressed controls (n = 346) by the method of tract-based spatial statistics. Subsequently, the potential role of pulse wave velocity as a mediator of depression- and age-related changes in extracted estimates of fractional anisotropy were analyzed.

The results of the tract-based analysis revealed significantly reduced fractional anisotropy in the left posterior thalamic radiation associated with depression. Analyses of extracted data indicated additional reductions of fractional anisotropy bilaterally in the posterior thalamic radiation and in the left sagittal stratum. The analyses of indirect effects did not show any significant mediation of depression-related effects on fractional anisotropy via pulse wave velocity. However, age-related effects on fractional anisotropy were partially mediated by pulse wave velocity.

In conclusion, major depressive disorder is associated with detrimental effects on cerebral white matter microstructure properties which are independent of vascular changes, as measured by pulse wave velocity. However, a portion of age-related detrimental effects on white matter is explained by vascular changes. Longitudinal studies are required for investigating changes in white matter and vascular parameters over time and their association with incident depression.

•

Depression is associated with reduced fractional anisotropy in the posterior thalamic radiation and in the sagittal stratum.

•

Pulse wave velocity is associated with reduced fractional anisotropy across the cerebral white matter.

•

Pulse wave velocity does not mediate depression-related effects on white matter microstructure.

•

Pulse wave velocity mediates a part of age-related detrimental effects on white matter microstructure.

Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample

In small, selected samples, an approach combining resting-state functional connectivity MRI and multivariate pattern analysis has been able to successfully classify patients diagnosed with unipolar depression. Purposes of this investigation were to assess the generalizability of this approach to a large clinically more realistic sample and secondarily to assess the replicability of previously reported methodological feasibility in a more homogeneous subgroup with pronounced depressive symptoms. Two independent subsets were drawn from the depression and control cohorts of the BiDirect study, each with 180 patients with and 180 controls without depression. Functional connectivity either among regions covering the gray matter or selected regions with known alterations in depression was assessed by resting-state fMRI. Support vector machines with and without automated feature selection were used to train classifiers differentiating between individual patients and controls in the entire first subset as well as in the subgroup. Model parameters were explored systematically. The second independent subset was used for validation of successful models. Classification accuracies in the large, heterogeneous sample ranged from 45.0 to 56.1% (chance level 50.0%). In the subgroup with higher depression severity, three out of 90 models performed significantly above chance (60.8-61.7% at independent validation). In conclusion, common classification methods previously successful in small homogenous depression samples do not immediately translate to a more realistic population. Future research to develop diagnostic classification approaches in depression should focus on more specific clinical questions and consider heterogeneity, including symptom severity as an important factor.