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Tim Hahn

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13 papers

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YNIMG Journal 2023 Journal Article

Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach

Julia-Katharina Pfarr
Tina Meller
Katharina Brosch
Frederike Stein
Florian Thomas-Odenthal
Ulrika Evermann
Adrian Wroblewski
Kai G. Ringwald

BACKGROUND: Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking. METHODS: In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories. RESULTS: Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors. CONCLUSIONS: Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.

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YNIMG Journal 2022 Journal Article

Recommendations for machine learning benchmarks in neuroimaging

Ramona Leenings
Nils R. Winter
Udo Dannlowski
Tim Hahn

The field of neuroimaging has embraced methods from machine learning in a variety of ways. Although an increasing number of initiatives have published open-access neuroimaging datasets, specifically designed benchmarks are rare in the field. In this article, we first describe how benchmarks in computer science and biomedical imaging have fostered methodological progress in machine learning. Second, we identify the special characteristics of neuroimaging data and outline what researchers have to ensure when establishing a neuroimaging benchmark, how datasets should be composed and how adequate evaluation criteria can be chosen. Based on lessons learned from machine learning benchmarks, we argue for an extended evaluation procedure that, next to applying suitable performance metrics, focuses on scientifically relevant aspects such as explainability, robustness, uncertainty, computational efficiency and code quality. Lastly, we envision a collaborative neuroimaging benchmarking platform that combines the discussed aspects in a collaborative and agile framework, allowing researchers across disciplines to work together on the key predictive problems of the field of neuroimaging and psychiatry.

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YNIMG Journal 2020 Journal Article

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Joaquim Radua
Eduard Vieta
Russell Shinohara
Peter Kochunov
Yann Quidé
Melissa J. Green
Cynthia S. Weickert
Thomas Weickert

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

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YNIMG Journal 2019 Journal Article

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

Michele Donini
João M. Monteiro
Massimiliano Pontil
Tim Hahn
Andreas J. Fallgatter
John Shawe-Taylor
Janaina Mourão-Miranda

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e. g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.

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YNIMG Journal 2017 Journal Article

Distributed patterns of occipito-parietal functional connectivity predict the precision of visual working memory

Elena M. Galeano Weber
Tim Hahn
Kirsten Hilger
Christian J. Fiebach

Limitations in visual working memory (WM) quality (i. e. , WM precision) may depend on perceptual and attentional limitations during stimulus encoding, thereby affecting WM capacity. WM encoding relies on the interaction between sensory processing systems and fronto-parietal ‘control’ regions, and differences in the quality of this interaction are a plausible source of individual differences in WM capacity. Accordingly, we hypothesized that the coupling between perceptual and attentional systems affects the quality of WM encoding. We combined fMRI connectivity analysis with behavioral modeling by fitting a variable precision and fixed capacity model to the performance data obtained while participants performed a visual delayed continuous response WM task. We quantified functional connectivity during WM encoding between occipital and parietal brain regions activated during both perception and WM encoding, as determined using a conjunction of two independent experiments. The multivariate pattern of voxel-wise inter-areal functional connectivity significantly predicted WM performance, most specifically the mean of WM precision but not the individual number of items that could be stored in memory. In particular, higher occipito-parietal connectivity was associated with higher behavioral mean precision. These results are consistent with a network perspective of WM capacity, suggesting that the efficiency of information flow between perceptual and attentional neural systems is a critical determinant of limitations in WM quality.

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YNIMG Journal 2015 Journal Article

Reliance on functional resting-state network for stable task control predicts behavioral tendency for cooperation

Tim Hahn
Karolien Notebaert
Christine Anderl
Philipp Reicherts
Matthias Wieser
Juliane Kopf
Andreas Reif
Katrin Fehl

Humans display individual variability in cooperative behavior. While an ever-growing body of research has investigated the neural correlates of task-specific cooperation, the mechanisms by which situation-independent, stable differences in cooperation render behavior consistent across a wide range of situations remain elusive. Addressing this issue, we show that the individual tendency to behave in a prosocial or individualistic manner can be predicted from the functional resting-state connectome. More specifically, connections of the cinguloopercular network which supports goal-directed behavior encode cooperative tendency. Effects of virtual lesions to this network on the efficacy of information exchange throughout the brain corroborate our findings. These results shed light on the neural mechanisms underlying individualists' and prosocials' habitual social decisions by showing that reliance on the cinguloopercular task-control network predicts stable cooperative behavior. Based on this evidence, we provide a unifying framework for the interpretation of functional imaging and behavioral studies of cooperative behavior.

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YNIMG Journal 2015 Journal Article

Sparse network-based models for patient classification using fMRI

Maria J. Rosa
Liana Portugal
Tim Hahn
Andreas J. Fallgatter
Marta I. Garrido
John Shawe-Taylor
Janaina Mourao-Miranda

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.

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YNIMG Journal 2013 Journal Article

The tricks of the trait: Neural implementation of personality varies with genotype-dependent serotonin levels

Tim Hahn
Sebastian Heinzel
Karolien Notebaert
Thomas Dresler
Andreas Reif
Klaus-Peter Lesch
Peter M. Jakob
Sabine Windmann

Gray's Reinforcement Sensitivity Theory (RST) has developed into one of the most prominent personality theories of the last decades. The RST postulates a Behavioral Inhibition System (BIS) modulating the reaction to stimuli indicating aversive events. A number of psychiatric disorders including depression, anxiety disorders, and psychosomatic illnesses have been associated with extreme BIS responsiveness. In recent years, neuroimaging studies have implicated the amygdala-septo-hippocampal circuit as an important neural substrate of the BIS. However, the neurogenetic basis of the regulation of this behaviorally and clinically essential system remains unclear. Investigating the effects of two functional genetic polymorphisms (tryptophan hydroxylase-2, G-703T, and serotonin transporter, serotonin transporter gene-linked polymorphic region) in 89 human participants, we find significantly different patterns of associations between BIS scores and amygdala–hippocampus connectivity during loss anticipation for genotype groups regarding both polymorphisms. Specifically, the correlation between amygdala–hippocampus connectivity and Gray's trait anxiety scores is positive in individuals homozygous for the TPH2 G-allele, while carriers of at least one T-allele show a negative association. Likewise, individuals homozygous for the 5-HTTLPR LA variant display a positive association while carriers of the S/LG allele show a trend towards a negative association. Thus, we show converging evidence of different neural implementation of the BIS depending on genotype-dependent levels of serotonin. We provide evidence suggesting that genotype-dependent serotonin levels and thus putative changes in the efficiency of serotonergic neurotransmission might not only alter brain activation levels directly, but also more fundamentally impact the neural implementation of personality traits. We outline the direct clinical implications arising from this finding and discuss the complex interplay of neural responses, genes and personality traits in this context.

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YNIMG Journal 2013 Journal Article

Variability of (functional) hemodynamics as measured with simultaneous fNIRS and fMRI during intertemporal choice

Sebastian Heinzel
Florian B. Haeussinger
Tim Hahn
Ann-Christine Ehlis
Michael M. Plichta
Andreas J. Fallgatter

Neural processing inferred from hemodynamic responses measured with functional near infrared spectroscopy (fNIRS) may be confounded with individual anatomical or systemic physiological sources of variance. This may hamper the validity of fNIRS signal interpretations and associations between individual traits and brain activation, such as the link between impulsivity-related personality traits and decreased prefrontal cognitive control during reward-based decision making. Hemodynamic responses elicited by an intertemporal choice reward task in 20 healthy subjects were investigated for multimodal correlations of simultaneous fNIRS–fMRI and for an impact of anatomy and scalp fMRI signal fluctuations on fNIRS signals. Moreover, correlations of prefrontal activation with trait “sensitivity to reward” (SR) were investigated for differences between methods. While showing substantial individual variability, temporal fNIRS–fMRI correlations increased with the activation, which both methods consistently detected within right inferior/middle frontal gyrus. Here, up to 41% of fNIRS channel activation variance was explained by individual gray matter volume simulated to be reached by near-infrared light, and up to 20% by scalp-cortex distance. Extracranial fMRI and fNIRS time series showed significant temporal correlations in the temple region. SR was negatively correlated with fMRI but not fNIRS activation elicited by immediate rewards of choice within right inferior/middle frontal gyrus. Higher SR increased the correlation between extracranial fMRI and fNIRS signals and decreased fNIRS–fMRI correlations. Task-related fNIRS signals might be impacted by regionally and individually weighted sources of anatomical and systemic physiological error variance. Trait-activation correlations might be affected or biased by systemic physiological responses, which should be accounted for in future fNIRS studies of interindividual differences.

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YNIMG Journal 2012 Journal Article

Randomness of resting-state brain oscillations encodes Gray's personality trait

Tim Hahn
Thomas Dresler
Ann-Christine Ehlis
Martin Pyka
Alica C. Dieler
Claudia Saathoff
Peter M. Jakob
Klaus-Peter Lesch

Randomness of functional Magnetic Resonance Imaging (fMRI) resting-state time-series has recently been used as a biomarker for numerous disorders including Alzheimer's and Parkinson's disease as well as autism. To date, however, it remains unknown whether and to what degree personality traits are associated with the randomness of resting-state temporal dynamics. To investigate this question, we estimated the Hurst exponent – a measure of the randomness of a time-series – during resting-state fMRI in brain areas previously associated with trait Impulsivity as defined in Gray's Reinforcement Sensitivity Theory of Personality in 15 healthy individuals. The Hurst exponent in the ventral striatum as well as in the orbitofrontal cortex (OFC) was significantly associated with the measure of Gray's trait Impulsivity. Specifically, more random resting-state neural dynamics corresponded to higher Impulsivity scores both in the ventral striatum (r(15)=−. 71; p=. 003) and the OFC (r(15)=−. 81; p<. 001). In summary, we provide evidence for an association between individual differences in Gray's Impulsivity and randomness in key areas of the reward system which have previously been associated with this personality trait. Based on evidence from fMRI and electroencephalographical studies, we suggest that this association might arise from resting-state fluctuations constraining task-related neural responsiveness. Thereby, we outline a potential mechanism linking randomness of resting-state dynamics and personality.

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YNIMG Journal 2011 Journal Article

NOS1 ex1f-VNTR polymorphism influences prefrontal brain oxygenation during a working memory task

Juliane Kopf
Martin Schecklmann
Tim Hahn
Thomas Dresler
Alica C. Dieler
Martin J. Herrmann
Andreas J. Fallgatter
Andreas Reif

Nitric oxide (NO) synthase produces NO, which serves as first and second messenger in neurons, where the protein is encoded by the NOS1 gene. A functional variable number of tandem repeats (VNTR) polymorphism in the promoter region of the alternative first exon 1f of NOS1 is associated with various functions of human behavior, for example increased impulsivity, while another, non-functional variant was linked to decreased verbal working memory and a heightened risk for schizophrenia. We therefore investigated the influence of NOS1 ex 1f-VNTR on working memory function as reflected by both behavioral measures and prefrontal oxygenation. We hypothesized that homozygous short allele carriers exhibit altered brain oxygenation in task-related areas, namely the dorsolateral and ventrolateral prefrontal cortex and the parietal cortex. To this end, 56 healthy subjects were stratified into a homozygous long allele group and a homozygous short allele group comparable for age, sex and intelligence. All subjects completed a letter n-back task (one-, two-, and three-back), while concentration changes of oxygenated (O2Hb) hemoglobin in the prefrontal cortex were measured with functional near-infrared spectroscopy (fNIRS). We found load-associated O2Hb increases in the prefrontal and parts of the parietal cortex. Significant load-associated oxygenation differences between the two genotype groups could be shown for the dorsolateral prefrontal cortex and the parietal cortex. Specifically, short allele carriers showed a significantly larger increase in oxygenation in all three n-back tasks. This suggests a potential compensatory mechanism, with task-related brain regions being more active in short allele carriers to compensate for reduced NOS1 expression.

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YNIMG Journal 2011 Journal Article

Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

Janaina Mourão-Miranda
David R. Hardoon
Tim Hahn
Andre F. Marquand
Steve C.R. Williams
John Shawe-Taylor
Michael Brammer

Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i. e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.

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YNIMG Journal 2009 Journal Article

Neural response to reward anticipation is modulated by Gray's impulsivity

Tim Hahn
Thomas Dresler
Ann-Christine Ehlis
Michael M. Plichta
Sebastian Heinzel
Thomas Polak
Klaus-Peter Lesch
Felix Breuer

According to the Reinforcement Sensitivity Theory (RST), Gray’s dimension of impulsivity, reflecting human trait reward sensitivity, determines the extent to which stimuli activate the Behavioural Approach System (BAS). The potential neural underpinnings of the BAS, however, remain poorly understood. In the present study, we examined the association between Gray’s impulsivity as defined by the RST and event-related fMRI BOLD-response to anticipation of reward in twenty healthy human subjects in brain regions previously associated with reward processing. Anticipation of reward during a Monetary Incentive Delay Task elicited activation in key components of the human reward circuitry such as the ventral striatum, the amygdala and the orbitofrontal cortex. Interindividual differences in Gray’s impulsivity accounted for a significant amount of variance of the reward-related BOLD-response in the ventral striatum and the orbitofrontal cortex. Specifically, higher trait reward sensitivity was associated with increased activation in response to cues indicating potential reward. Extending previous evidence, here we show that variance in functional brain activation during anticipation of reward is attributed to interindividual differences regarding Gray’s dimension of impulsivity. Thus, trait reward sensitivity contributes to the modulation of responsiveness in major components of the human reward system which thereby display a core property of the BAS. Generally, fostering our understanding of the neural underpinnings of the association of reward-related interindividual differences in affective traits might aid researchers in quest for custom-tailored treatments of psychiatric disorders, further disentangling the complex relationship between personality traits, emotion, and health.

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