Biology subjects

Hoffstaedter, F.

Publications and source records attributed to Hoffstaedter, F..

36 records · Page 2Linked to original sources

bioRxiv2023

A systematic comparison of VBM pipelines and their application to age prediction

Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.

neuroscience↗

bioRxiv2022

BrainAGE revisited: Keeping up the pace with deep learning methods

Since the introduction of the BrainAGE method (Franke et al., 2010), novel machine learning methods of brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for 5 brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimers disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.

neuroscience↗

bioRxiv2022

Brain-age prediction: a systematic comparison of machine learning workflows

The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-site accuracy, (2) cross-site generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-site mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-site MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-site and cross-site predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimers and mild cognitive impairment patients. However, in the presence of age bias, the delta estimates in the diseased population varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application. HighlightsO_LIThere is an effect of both feature space and ML algorithm on prediction error. C_LIO_LIVoxel-wise features performed better than parcel-wise features. C_LIO_LIGPR, KRR and RVR algorithms performed well. C_LIO_LIThe within-site and cross-site delta-behavior correlations disagree. C_LIO_LIHigher brain-age delta inference in AD depends on data used for bias correction. C_LI

neuroscience↗

bioRxiv2022

Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity

Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.

neuroscience↗

bioRxiv2022

The Uniqueness of Human Vulnerability to Brain Aging in Great Ape Evolution

Aging is associated with robust decline of the brains gray matter. This spatially specific, morphological change in humans has recently been found in chimpanzees. Direct comparison of age-related brain deterioration between these great ape species can provide a unique evolutionary perspective on human brain aging. Here, we present a data-driven, cross-species comparative framework to explore the relationship between gray matter atrophy with age and cross-species cerebral expansion in chimpanzees and humans. In humans, we found a positive relationship between cerebral aging and cortical expansion, whereas, in chimpanzees no such relationship was found. The greater aging and expansion effects in higher-order cognitive regions like the orbito-frontal cortex were observed to be unique to humans. This resembles the last in, first out hypothesis for neurodevelopment and may represent a biological cost for recent evolutionary developments of human faculties.

neuroscience↗

bioRxiv2022

Accurate sex prediction of cisgender and transgender individuals without brain size bias

Brain size differs substantially between human males and females. This difference in total intracranial volume (TIV) can cause bias when employing machine-learning approaches for the investigation of sex differences in brain morphology. TIV-biased models will likely not capture actual qualitative sex differences in brain organization but rather learn to classify an individuals sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. Here, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for brain size either through featurewise confound removal or by matching training samples for TIV. Our results provide evidence that non-TIV-biased models can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modelling to avoid bias in automated decision making. TeaserAccurate non-biased structural sex classification in cis- and transgender individuals by matching training samples for TIV

neuroscience↗

bioRxiv2021

FAIRly big: A framework for computationally reproducible processing of large-scale data

Large-scale datasets present unique opportunities to perform scientific investigations with un-precedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the frameworks performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

bioinformatics↗

bioRxiv2021

Tract-specific statistics based on diffusion-weighted probabilistic tractography

Diffusion-weighted neuroimaging approaches provide rich evidence with which to estimate the structural integrity of white matter in vivo. Tract-based spatial statistics (TBSS), for instance, allows us to assess the spatial distribution of statistical tests performed on diffusion properties such as fractional anisotropy, which are proposed to reflect the integrity of white matter tracts. However, such methods do not provide a direct assessment of white matter integrity for connections between two specific regions of the brain. Here, we present a novel method for deriving tract-specific diffusion statistics, based upon arbitrarily-defined regions of interest. Our approach makes use of an empirically-derived population distribution based on probabilistic tractography, using the Nathan Kline Institute (NKI) Enhanced Rockland sample. We use a heuristic method to determine the most likely geometry of a path between two regions, if one exists, and express this as a spatial distribution. We then estimate the average orientation of streamlines traversing this path, at discrete distances along its trajectory, and compute the fraction of diffusion directed along this orientation for each participant. This allows us to obtain participant-wise metrics along the specific tract (tract-specific anisotropy; TSA), which can then be used to perform statistical analysis on any comparable population. Based on this method, we report both negative and positive associations between age and TSA for two networks derived from published meta-analytic studies (the "default mode" and "what-where" networks), along with more moderate sex differences and age-by-sex interactions in both networks.

neuroscience↗

bioRxiv2020

The Aging Brain and Executive Functions Revisited: Implications from Meta-Analytic and Functional-Connectivity Evidence

Healthy aging is associated with changes in cognitive performance including executive functions (EFs) and their associated brain activation patterns. However, it has remained unclear which EF-related brain regions are affected consistently, because the results of pertinent neuroimaging studies and earlier meta-analyses vary considerably. We, therefore, conducted new rigorous meta-analyses of published age differences in EF-related brain activity. Out of a larger set of regions associated with EFs, only left inferior frontal junction (IFJ) and left anterior cuneus/precuneus (aC/PrC) were found to show consistent age differences. To further characterize these two age-sensitive regions, we performed seed-based resting-state functional connectivity (RS-FC) analyses using fMRI data from a large adult sample with a wide age range. We also assessed associations of the two regions whole-brain RS-FC patterns with age and EF performance. Although functional profiling and RS-FC analyses point towards a domain-general role of left IFJ in EFs, the pattern of individual study contributions to the meta-analytic results suggests process-specific modulations by age. Our analyses further indicate that left aC/PrC is recruited differently by older (compared to younger) adults during EF tasks, potentially reflecting inefficiencies in switching the attentional focus. Overall, our findings question earlier meta-analytic results and suggest a larger heterogeneity of age-related differences in brain activity associated with EFs. Hence, they encourage future research that pays greater attention to replicability, investigates age-related differences in deactivation, and focuses on more narrowly defined EF subprocesses, combining multiple behavioral assessments with multi-modal imaging. Highlights- Healthy aging is linked to deterioration in executive functions (EFs) - ALE meta-analyses examined consistent age differences in brain activity linked to EFs - In a larger set of EF regions, only left IFJ and (pre)cuneus were sensitive to age - Advanced age was linked to weaker functional coupling within EF-related networks - Our findings question earlier meta-analytic findings

neuroscience↗

bioRxiv2020

Connectivity patterns of task-specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to molecular architecture

BackgroundDespite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. MethodsWe investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior molecular imaging in healthy populations. ResultsTen-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. ConclusionsWe revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

neuroscience↗

bioRxiv2020

Chimpanzee Brain Morphometry Utilizing Standardized MRI Preprocessing and Macroanatomical Annotations

Chimpanzees are among the closest living relatives to humans and, as such, provide a crucial comparative model for investigating primate brain evolution. In recent years, human brain mapping has strongly benefited from enhanced computational models and image processing pipelines that could also improve data analyses in animals by using species-specific templates. In this study, we use structural MRI data from the National Chimpanzee Brain Resource (NCBR) to develop the chimpanzee brain reference template Juna.Chimp for spatial registration and the macro-anatomical brain parcellation Davi130 for standardized whole-brain analysis. Additionally, we introduce a ready-to-use image processing pipeline built upon the CAT12 toolbox in SPM12, implementing a standard human image preprocessing framework in chimpanzees. Applying this approach to data from 178 subjects, we find strong evidence for age-related GM atrophy in multiple regions of the chimpanzee brain, as well as, a human-like anterior-posterior pattern of hemi-spheric asymmetry in medial chimpanzee brain regions.

neuroscience↗

bioRxiv2020

A Connectivity-based Psychometric Prediction Framework for Brain-behavior Relationship Studies

The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions connectivity profiles. We first illustrate two main applications: 1) single brain regions predictive power for a range of psychometric variables, and 2) single psychometric variables predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.

neuroscience↗

bioRxiv2019

Genetic factors influencing a neurobiological substrate for psychiatric disorders

A retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex - areas that constitute hub nodes of the salience network - represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n=2,271), followed by meta-analysis and replication in a fifth cohort (n=865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

genetics↗

bioRxiv2019

PHENOTYPIC AND GENETIC CORRELATION BETWEEN SLEEP, BEHAVIOR, AND MACROSCALE CORTICAL GREY MATTER

Humans need about 7 to 9 hours of sleep per night. Sleep habits are heritable, associated with brain function and structure, and intrinsically related to well-being, mental and physical health. This raises the question whether associations between sleep, mental and physical health can be attributed to a shared macroscale neurobiology.\n\nCombining neuroimaging and behavioral genetic approaches in two independent large-scale datasets (n=1887) we demonstrate phenotypic and genetic correspondence between sleep, intelligence, and BMI. Sleep was associated with local thickness variation in frontal, temporal, and occipital cortices. Using a comprehensive multivariate approach, we identified two robust latent components highlighting the interdigitation of sleep, intelligence, BMI, and depression and their shared relation to regions in unimodal and heteromodal association cortices. Latent relationships were heritable and driven by shared additive genetic factors. These observations provide a system-level perspective on the interrelation of sleep, mental, and physical conditions, anchored in grey-matter neuroanatomy.

neuroscience↗

bioRxiv2019

Neurogenetic basis of personality

Local variation in cortical architecture is highly heritable and distinct genes are associated with specific cortical regions. Total surface area has been shown to be genetically correlated with complex cognitive capacities, suggesting cortical brain structure is a viable endophenotype linking genes to behavior. However, to what extend local brain structure has a genetic association with cognitive and emotional functioning is incompletely understood. Here, we study the genetic correlation between personality traits and local cortical structure in a large-scale twin sample (Human Connectome Project, n=1106, 22-37y). We found a genetic overlap between personality traits and local cortical structure in 10 of 17 observed phenotypic associations in predominantly frontal cortices. To evaluate the robustness of observed personality-brain associations we studied two independent age-matched samples (GSP: n=926, age=19-35y, eNKI: n=210, age: 19-39y). We observed anecdotal to moderate evidence for a successful replication of the negative association between surface area in medial prefrontal cortex and Neuroticism in both samples. Quantitative functional decoding indicated this region is implicated in emotional and socio-cognitive functional processes. In sum, our observations suggest that associations between local brain structure and personality are, in part, under genetic control. However, associations are weak and only the relation between frontal surface area and Neuroticism was consistently observed across three independent samples of young adults.

neuroscience↗

bioRxiv2019

Sex classification by resting state brain connectivity

1A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants sex can be classified based on spatially specific resting state (RS) brain-connectivity, using two samples from the Human Connectome Project (n1 = 434, n2 = 310) and one fully independent sample from the 1000BRAINS study (n=941). The classifier, which was trained on one sample and tested on the other two, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporo-parietal regions, insula and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

neuroscience↗

bioRxiv2018

Empirical examination of the replicability of associations between brain structure and psychological variables

Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the previously-reported \"structural brain behavior\" (SBB)-associations has been questioned, recently. Here, we conducted an empirical investigation, assessing replicability of SBB among heathy adults. For a wide range of psychological measures, the replicability of associations with gray matter volume was assessed. Our results revealed that among healthy individuals 1) finding an association between performance at standard psychological tests and brain morphology is relatively unlikely 2) significant associations, found using an exploratory approach, have overestimated effect sizes and 3) can hardly be replicated in an independent sample. After considering factors such as sample size and comparing our findings with more replicable SBB-associations in a clinical cohort and replicable associations between brain structure and non-psychological phenotype, we discuss the potential causes and consequences of these findings.

neuroscience↗

bioRxiv2017

Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis

INTRODUCTIONWe performed a systematic review and meta-analysis of the Alzheimers disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment (MCI), using resting-state functional magnetic resonance imaging (rsfMRI).\n\nMETHODSStudies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies.\n\nRESULTSThirty-four studies were included (1,363 participants, average 40 per study). Consistent alterations in connectivity were found in the default-mode, salience and limbic networks in patients with AD dementia, MCI, or in both groups. We also identified a bias in the literature towards specific examination of the default-mode network.\n\nDISCUSSIONConvergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that metabolically expensive hub regions in the brain might be an early target of AD.

neuroscience↗