Tim Hahn

Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging

Functional near-infrared spectroscopy (fNIRS) is an established optical neuroimaging method for measuring functional hemodynamic responses to infer neural activation. However, the impact of individual anatomy on the sensitivity of fNIRS measuring hemodynamics within cortical gray matter is still unknown. By means of Monte Carlo simulations and structural MRI of 23 healthy subjects (mean age: years), we characterized the individual distribution of tissue-specific NIR-light absorption underneath 24 prefrontal fNIRS channels. We, thereby, investigated the impact of scalp-cortex distance (SCD), frontal sinus volume as well as sulcal morphology on gray matter volumes () traversed by NIR-light, i.e. anatomy-dependent fNIRS sensitivity. The NIR-light absorption between optodes was distributed describing a rotational ellipsoid with a mean penetration depth of considering the deepest of light. Of the detected photon packages scalp and bone absorbed and absorbed of the energy. The mean volume was negatively correlated () with the SCD and frontal sinus volume () and was reduced by in subjects with relatively large compared to small frontal sinus. Head circumference was significantly positively correlated with the mean SCD () and the traversed frontal sinus volume (). Sulcal morphology had no significant impact on . Our findings suggest to consider individual SCD and frontal sinus volume as anatomical factors impacting fNIRS sensitivity. Head circumference may represent a practical measure to partly control for these sources of error variance.

Genome-wide copy number variation analysis in attention-deficit/hyperactivity disorder: association with neuropeptide Y gene dosage in an extended pedigree

Attention-deficit/hyperactivity disorder (ADHD) is a common, highly heritable neurodevelopmental syndrome characterized by hyperactivity, inattention and increased impulsivity. To detect micro-deletions and micro-duplications that may have a role in the pathogenesis of ADHD, we carried out a genome-wide screen for copy number variations (CNVs) in a cohort of 99 children and adolescents with severe ADHD. Using high-resolution array comparative genomic hybridization (aCGH), a total of 17 potentially syndrome-associated CNVs were identified. The aberrations comprise 4 deletions and 13 duplications with approximate sizes ranging from 110 kb to 3 Mb. Two CNVs occurred de novo and nine were inherited from a parent with ADHD, whereas five are transmitted by an unaffected parent. Candidates include genes expressing acetylcholine-metabolizing butyrylcholinesterase (BCHE), contained in a de novo chromosome 3q26.1 deletion, and a brain-specific pleckstrin homology domain-containing protein (PLEKHB1), with an established function in primary sensory neurons, in two siblings carrying a 11q13.4 duplication inherited from their affected mother. Other genes potentially influencing ADHD-related psychopathology and involved in aberrations inherited from affected parents are the genes for the mitochondrial NADH dehydrogenase 1 α subcomplex assembly factor 2 (NDUFAF2), the brain-specific phosphodiesterase 4D isoform 6 (PDE4D6) and the neuronal glucose transporter 3 (SLC2A3). The gene encoding neuropeptide Y (NPY) was included in a ∼3 Mb duplication on chromosome 7p15.2-15.3, and investigation of additional family members showed a nominally significant association of this 7p15 duplication with increased NPY plasma concentrations (empirical family-based association test, P=0.023). Lower activation of the left ventral striatum and left posterior insula during anticipation of large rewards or losses elicited by functional magnetic resonance imaging links gene dose-dependent increases in NPY to reward and emotion processing in duplication carriers. These findings implicate CNVs of behaviour-related genes in the pathogenesis of ADHD and are consistent with the notion that both frequent and rare variants influence the development of this common multifactorial syndrome.

Integrating Neurobiological Markers of Depression

CONTEXT Although psychiatric disorders are, to date, diagnosed on the basis of behavioral symptoms and course of illness, the interest in neurobiological markers of psychiatric disorders has grown substantially in recent years. However, current classification approaches are mainly based on data from a single biomarker, making it difficult to predict disorders characterized by complex patterns of symptoms. OBJECTIVE To integrate neuroimaging data associated with multiple symptom-related neural processes and demonstrate their utility in the context of depression by deriving a predictive model of brain activation. DESIGN Two groups of participants underwent functional magnetic resonance imaging during 3 tasks probing neural processes relevant to depression. SETTING Participants were recruited from the local population by use of advertisements; participants with depression were inpatients from the Department of Psychiatry, Psychosomatics, and Psychotherapy at the University of Wuerzburg, Wuerzburg, Germany. PARTICIPANTS We matched a sample of 30 medicated, unselected patients with depression by age, sex, smoking status, and handedness with 30 healthy volunteers. MAIN OUTCOME MEASURE Accuracy of single-subject classification based on whole-brain patterns of neural responses from all 3 tasks. RESULTS Integrating data associated with emotional and affective processing substantially increases classification accuracy compared with single classifiers. The predictive model identifies a combination of neural responses to neutral faces, large rewards, and safety cues as nonredundant predictors of depression. Regions of the brain associated with overall classification comprise a complex pattern of areas involved in emotional processing and the analysis of stimulus features. CONCLUSIONS Our method of integrating neuroimaging data associated with multiple, symptom-related neural processes can provide a highly accurate algorithm for classification. The integrated biomarker model shows that data associated with both emotional and reward processing are essential for a highly accurate classification of depression. In the future, large-scale studies will need to be conducted to determine the practical applicability of our algorithm as a biomarker-based diagnostic aid.

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

According to the Reinforcement Sensitivity Theory (RST), Grays 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 Grays 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 Grays 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 Grays 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.

Sparse network-based models for patient classification using fMRI

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has been successful at discriminating psychiatric patients from healthy subjects. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. As is generally accepted, many psychiatric disorders, such as depression and schizophrenia, are brain connectivity disorders. Therefore, pattern recognition based on network models should provide more scientific insight and potentially more powerful predictions than voxel-based approaches. Here, we build a sparse network-based discriminative modelling framework, based on Gaussian graphical models and L1-norm regularised linear Support Vector Machines (SVM). The proposed framework provides easier pattern interpretation in terms of underlying network changes between groups, and we illustrate our technique by classifying patients with depression and controls, using fMRI data from a sad facial processing task.

Association between reward-related activation in the ventral striatum and trait reward sensitivity is moderated by dopamine transporter genotype

The impact of individual differences on human reward processing has been a focus of research in recent years, particularly, as they are associated with a variety of neuropsychiatric diseases including addiction and attention‐deficit/hyperactivity disorder. Studies exploring the neural basis of individual differences in reward sensitivity have consistently implicated the ventral striatum (VS) as a core component of the human reward system. However, the mechanisms of dopaminergic neurotransmission underlying ventral striatal activation as well as trait reward sensitivity remain speculative. We addressed this issue by investigating the triadic interplay between VS reactivity during reward anticipation using functional magnetic resonance imaging, trait reward sensitivity, and dopamine (DA) transporter genotype (40‐bp 3′VNTR of DAT, SLC6A3) affecting synaptic DA neurotransmission. Our results show that DAT variation moderates the association between VS‐reactivity and trait reward sensitivity. Specifically, homozygote carriers of the DAT 10‐repeat allele exhibit a strong positive correlation between reward sensitivity and reward‐related VS activity whereas this relationship is absent in the DAT 9‐repeat allele carriers. We discuss the possibility that this moderation of VS‐trait relation might arise from DAT‐dependent differences in DA availability affecting synaptic plasticity within the VS. Generally, studying the impact of dopaminergic gene variations on the relation between reward‐related brain activity and trait reward sensitivity might facilitate the investigation of complex mechanisms underlying disorders linked to dysregulation of DA neurotransmission. Hum Brain Mapp, 2010.

Predicting Treatment Response to Cognitive Behavioral Therapy in Panic Disorder With Agoraphobia by Integrating Local Neural Information

IMPORTANCE Although neuroimaging research has made substantial progress in identifying the large-scale neural substrate of anxiety disorders, its value for clinical application lags behind expectations. Machine-learning approaches have predictive potential for individual-patient prognostic purposes and might thus aid translational efforts in psychiatric research. OBJECTIVE To predict treatment response to cognitive behavioral therapy (CBT) on an individual-patient level based on functional magnetic resonance imaging data in patients with panic disorder with agoraphobia (PD/AG). DESIGN, SETTING, AND PARTICIPANTS We included 49 patients free of medication for at least 4 weeks and with a primary diagnosis of PD/AG in a longitudinal study performed at 8 clinical research institutes and outpatient centers across Germany. The functional magnetic resonance imaging study was conducted between July 2007 and March 2010. INTERVENTIONS Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure. MAIN OUTCOMES AND MEASURES Treatment response was defined as exceeding a 50% reduction in Hamilton Anxiety Rating Scale scores. Blood oxygenation level-dependent signal was measured during a differential fear-conditioning task. Regional and whole-brain gaussian process classifiers using a nested leave-one-out cross-validation were used to predict the treatment response from data acquired before CBT. RESULTS Although no single brain region was predictive of treatment response, integrating regional classifiers based on data from the acquisition and the extinction phases of the fear-conditioning task for the whole brain yielded good predictive performance (accuracy, 82%; sensitivity, 92%; specificity, 72%; P < .001). Data from the acquisition phase enabled 73% correct individual-patient classifications (sensitivity, 80%; specificity, 67%; P < .001), whereas data from the extinction phase led to an accuracy of 74% (sensitivity, 64%; specificity, 83%; P < .001). Conservative reanalyses under consideration of potential confounders yielded nominally lower but comparable accuracy rates (acquisition phase, 70%; extinction phase, 71%; combined, 79%). CONCLUSIONS AND RELEVANCE Predicting treatment response to CBT based on functional neuroimaging data in PD/AG is possible with high accuracy on an individual-patient level. This novel machine-learning approach brings personalized medicine within reach, directly supporting clinical decisions for the selection of treatment options, thus helping to improve response rates.

Differential prefrontal and frontotemporal oxygenation patterns during phonemic and semantic verbal fluency

Movement artifacts are still considered a problematic issue for imaging research on overt language production. This motion-sensitivity can be overcome by functional near-infrared spectroscopy (fNIRS). In the present study, 50 healthy subjects performed a combined phonemic and semantic overt verbal fluency task while frontal and temporal cortex oxygenation was recorded using multi-channel fNIRS. Results showed a partial dissociation for phonemic and semantic word generation with equally increased oxygenation in frontotemporal cortices for both types of tasks whereas anterior and superior prefrontal areas were exclusively activated during phonemic fluency. Also, a general left-lateralization was found being more pronounced during semantic processing. These findings line up with earlier imaging and lesion studies emphasizing a crucial role of the temporal lobe for semantic word production, whereas phonemic processing seems to depend on intact frontal lobe function.

Functional Amygdala-Hippocampus Connectivity During Anticipation of Aversive Events is Associated with Gray's Trait “Sensitivity to Punishment”

BACKGROUND The reinforcement sensitivity theory postulates a behavioral inhibition system that modulates reaction to stimuli indicating aversive events. Grays dimension of anxiety, reflecting human trait sensitivity to aversive events, determines the extent to which stimuli activate the behavioral inhibition system. Although structural brain imaging has previously identified the amygdala and the hippocampus as two major components related to the behavioral inhibition system, the functional dynamics of the responses in these structures remain unclear. METHODS In this study, we examined the event-related functional magnetic resonance imaging blood oxygen level-dependent response in the hippocampus and amygdala as well as the functional connectivity of the two regions during anticipation of monetary loss in 45 healthy human subjects. RESULTS Anticipation of loss elicited activation in the hippocampus as well as in the amygdala. Additionally, substantial functional connectivity between the two areas was observed. Furthermore, this functional connectivity was significantly correlated with individual differences in Grays trait sensitivity to aversive events. Specifically, higher trait sensitivity to aversive events was associated with increased functional connectivity following cues indicating potential loss. CONCLUSIONS In summary, we show that individual differences regarding Grays trait sensitivity to aversive events as defined by the reinforcement sensitivity theory are associated with the neural dynamics of the amygdala-hippocampal circuit during anticipation of aversive events. In particular, evidence is provided for a relationship between functional brain imaging data and a psychometric approach specifically measuring Grays trait sensitivity to aversive events, thereby potentially identifying the neural substrate of the behavioral inhibition system.

SCoRS—A Method Based on Stability for Feature Selection and Mapping in Neuroimaging

Feature selection (FS) methods play two important roles in the context of neuroimaging based classification: potentially increase classification accuracy by eliminating irrelevant features from the model and facilitate interpretation by identifying sets of meaningful features that best discriminate the classes. Although the development of FS techniques specifically tuned for neuroimaging data is an active area of research, up to date most of the studies have focused on finding a subset of features that maximizes accuracy. However, maximizing accuracy does not guarantee reliable interpretation as similar accuracies can be obtained from distinct sets of features. In the current paper we propose a new approach for selecting features: SCoRS (survival count on random subsamples) based on a recently proposed Stability Selection theory. SCoRS relies on the idea of choosing relevant features that are stable under data perturbation. Data are perturbed by iteratively sub-sampling both features (subspaces) and examples. We demonstrate the potential of the proposed method in a clinical application to classify depressed patients versus healthy individuals based on functional magnetic resonance imaging data acquired during visualization of happy faces.