Controlling the human connectome with spatially diffuse input signals.

The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.

Molecular signatures of attention networks.

Attention network theory proposes three distinct types of attention-alerting, orienting, and control-that are supported by separate brain networks and modulated by different neurotransmitters, that is, norepinephrine, acetylcholine, and dopamine. Here, we explore the extent of cortical, genetic, and molecular dissociation of these three attention systems using multimodal neuroimaging. We evaluated the spatial overlap between fMRI activation maps from the attention network test (ANT) and cortex-wide gene expression data from the Allen Human Brain Atlas. The goal was to identify genes associated with each of the attention networks in order to determine whether specific groups of genes were co-expressed with the corresponding attention networks. Furthermore, we analyzed publicly available PET-maps of neurotransmitter receptors and transporters to investigate their spatial overlap with the attention networks. Our analyses revealed a substantial number of genes (3871 for alerting, 6905 for orienting, 2556 for control) whose cortex-wide expression co-varied with the activation maps, prioritizing several molecular functions such as the regulation of protein biosynthesis, phosphorylation, and receptor binding. Contrary to the hypothesized associations, the ANT activation maps neither aligned with the distribution of norepinephrine, acetylcholine, and dopamine receptor and transporter molecules, nor with transcriptomic profiles that would suggest clearly separable networks. Independence of the attention networks appeared additionally constrained by a high level of spatial dependency between the network maps. Future work may need to reconceptualize the attention networks in terms of their segregation and reevaluate the presumed independence at the neural and neurochemical level.

Depressive inclinations mediate the association between personality (neuroticism/conscientiousness) and TikTok Use Disorder tendencies.

BACKGROUND: We introduce a novel measure for assessing TikTok overuse, called the TikTok Use Disorder-Questionnaire (TTUD-Q). As part of ongoing investigations into the suitability of the World Health Organization's (WHO) framework for diagnosing Gaming Disorder in the context of social media overuse, we developed this questionnaire by adapting the WHO framework, replacing the term "gaming" with "TikTok use". METHODS: In order to address this question, we investigated the psychometric properties of the newly designed TTUD-Q and assessed its associations with the BFI-10 (assessing the Big Five of Personality) and the PHQ-8 (assessing depressive tendencies). RESULTS: In this study, involving a final sample of 378 participants, we observed that higher levels of neuroticism were linked to greater tendencies toward TikTok Use Disorder (TTUD). Furthermore, we identified that this association was mediated by depressive tendencies. Similar trends emerged when investigating the relationship between lower levels of conscientiousness and higher TTUD tendencies, with depressive tendencies once again serving as a mediator. DISCUSSION: Our research sets the foundation for future studies that should delve deeper into examining individual differences in TTUD using the WHO framework originally designed for Gaming Disorder.

White Matter Tract Integrity Is Reduced in Depression and in Individuals With Genetic Liability to Depression.

BACKGROUND: While major depression has been linked to changes in white matter architecture, it remains unclear whether risk factors for depression are directly associated with these alterations. We reexamined white matter fiber tracts in individuals with depressive symptoms and investigated the connection between genetic and environmental risk for depression and structural changes in the brain. METHODS: We included 19,183 participants from the UK Biobank imaging cohort, with depression status and adverse life experience based on questionnaire data and genetic liability for depression quantified by polygenic scores. The integrity of 27 white matter tracts was assessed using mean fractional anisotropy derived from diffusion magnetic resonance imaging. RESULTS: White matter integrity was reduced, particularly in thalamic and intracortical fiber tracts, in individuals with depressive symptoms, independent of current symptom status. In a group of healthy individuals without depression, increasing genetic risk and increasing environmental risk were associated with reduced integrity in relevant fiber tracts, particularly in thalamic radiations. This association was stronger than expected based on statistical dependencies between samples, as confirmed by subsequent in silico simulations and permutation tests. CONCLUSIONS: White matter alterations in thalamic and association tracts are associated with depressive symptoms and genetic risk for depression in unaffected individuals, suggesting an intermediate phenotype at the brain level.

Social media use and everyday cognitive failure: investigating the fear of missing out and social networks use disorder relationship.

BACKGROUND: Nearly five billion individuals worldwide are using social media platforms. While the benefits of using social media, such as fostering social connections, are clear, ongoing discussions are focused on whether excessive use of these platforms might have adverse effects on cognitive functioning. Excessive social media use shares similarities with addictive behaviors and is believed to result from a complex interplay of individual characteristics, emotions, thoughts, and actions. Among these contributing factors, one of particular interest is the Fear of Missing Out (FoMO), a state where an individual apprehends that others are experiencing rewarding moments in their absence (but see more information on the FoMO trait/state debate in this article). METHODS: In this study, we aimed to explore the intricate relationships between FoMO, tendencies towards Social Networks Use Disorder (SNUD), and everyday cognitive failures. To achieve this, we gathered a large sample of N = 5314 participants and administered a comprehensive set of questionnaires. These included a Fear of Missing Out (FoMO) scale, which assessed both trait and state aspects of FoMO, the Social Networking Sites-Addiction Test (SNS-AT), designed to gauge tendencies towards SNUD, and the Cognitive Failure Questionnaire (CFQ), which measured everyday cognitive lapses. RESULTS: Our findings revealed that among non-users of social media, both FoMO and everyday cognitive failures were at their lowest levels. Further, in the group of social media users, we observed a significant relationship between FoMO and cognitive failures, which was mediated by SNUD tendencies. This mediation was particularly pronounced for the state component of FoMO, which encompasses maladaptive thoughts related to online behavior. CONCLUSIONS: While our study is cross-sectional and thus cannot establish causality, one plausible interpretation of our findings is that higher FoMO tendencies may trigger excessive social media use, which in turn could lead to cognitive failures, possibly due to distraction and reduced attention to everyday tasks.

Enhancing precision in human neuroscience.

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

Computational mechanisms of belief updating in relation to psychotic-like experiences.

Introduction: Psychotic-like experiences (PLEs) may occur due to changes in weighting prior beliefs and new evidence in the belief updating process. It is still unclear whether the acquisition or integration of stable beliefs is altered, and whether such alteration depends on the level of environmental and belief precision, which reflects the associated uncertainty. This motivated us to investigate uncertainty-related dynamics of belief updating in relation to PLEs using an online study design. Methods: We selected a sample (n = 300) of participants who performed a belief updating task with sudden change points and provided self-report questionnaires for PLEs. The task required participants to observe bags dropping from a hidden helicopter, infer its position, and dynamically update their belief about the helicopter's position. Participants could optimize performance by adjusting learning rates according to inferred belief uncertainty (inverse prior precision) and the probability of environmental change points. We used a normative learning model to examine the relationship between adherence to specific model parameters and PLEs. Results: PLEs were linked to lower accuracy in tracking the outcome (helicopter location) (ß = 0.26 ± 0.11, p = 0.018) and to a smaller increase of belief precision across observations after a change point (ß = -0.003 ± 0.0007, p < 0.001). PLEs were related to slower belief updating when participants encountered large prediction errors (ß = -0.03 ± 0.009, p = 0.001). Computational modeling suggested that PLEs were associated with reduced overall belief updating in response to prediction errors (ßPE = -1.00 ± 0.45, p = 0.028) and reduced modulation of updating at inferred environmental change points (ßCPP = -0.84 ± 0.38, p = 0.023). Discussion: We conclude that PLEs are associated with altered dynamics of belief updating. These findings support the idea that the process of balancing prior belief and new evidence, as a function of environmental uncertainty, is altered in PLEs, which may contribute to the development of delusions. Specifically, slower learning after large prediction errors in people with high PLEs may result in rigid beliefs. Disregarding environmental change points may limit the flexibility to establish new beliefs in the face of contradictory evidence. The present study fosters a deeper understanding of inferential belief updating mechanisms underlying PLEs.

Characterizing functional modules in the human thalamus: coactivation-based parcellation and systems-level functional decoding.

The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II.

From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's biological and chronological age of the brain ("brain age gap") can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.

Trajectory of rich club properties in structural brain networks.

Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8066 participants aged 5-80 years. Across all brain regions, structural connectivity strength followed an "inverted-U"-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a "first come, last served" model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.

Systems-level decoding reveals the cognitive and behavioral profile of the human intraparietal sulcus.

Introduction: The human intraparietal sulcus (IPS) covers large portions of the posterior cortical surface and has been implicated in a variety of cognitive functions. It is, however, unclear how cognitive functions dissociate between the IPS's heterogeneous subdivisions, particularly in perspective to their connectivity profile. Methods: We applied a neuroinformatics driven system-level decoding on three cytoarchitectural distinct subdivisions (hIP1, hIP2, hIP3) per hemisphere, with the aim to disentangle the cognitive profile of the IPS in conjunction with functionally connected cortical regions. Results: The system-level decoding revealed nine functional systems based on meta-analytical associations of IPS subdivisions and their cortical coactivations: Two systems-working memory and numeric cognition-which are centered on all IPS subdivisions, and seven systems-attention, language, grasping, recognition memory, rotation, detection of motions/shapes and navigation-with varying degrees of dissociation across subdivisions and hemispheres. By probing the spatial overlap between systems-level co-activations of the IPS and seven canonical intrinsic resting state networks, we observed a trend toward more co-activation between hIP1 and the front parietal network, between hIP2 and hIP3 and the dorsal attention network, and between hIP3 and the visual and somatomotor network. Discussion: Our results confirm previous findings on the IPS's role in cognition but also point to previously unknown differentiation along the IPS, which present viable starting points for future work. We also present the systems-level decoding as promising approach toward functional decoding of the human connectome.

Attention networks and the intrinsic network structure of the human brain.

Attention network theory distinguishes three independent systems, each supported by its own distributed network: an alerting network to deploy attentional resources in anticipation, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. The brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks, which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

Personality network neuroscience: Promises and challenges on the way toward a unifying framework of individual variability.

We propose that the application of network theory to established psychological personality conceptions has great potential to advance a biologically plausible model of human personality. Stable behavioral tendencies are conceived as personality "traits." Such traits demonstrate considerable variability between individuals, and extreme expressions represent risk factors for psychological disorders. Although the psychometric assessment of personality has more than hundred years tradition, it is not yet clear whether traits indeed represent "biophysical entities" with specific and dissociable neural substrates. For instance, it is an open question whether there exists a correspondence between the multilayer structure of psychometrically derived personality factors and the organizational properties of traitlike brain systems. After a short introduction into fundamental personality conceptions, this article will point out how network neuroscience can enhance our understanding about human personality. We will examine the importance of intrinsic (task-independent) brain connectivity networks and show means to link brain features to stable behavioral tendencies. Questions and challenges arising from each discipline itself and their combination are discussed and potential solutions are developed. We close by outlining future trends and by discussing how further developments of network neuroscience can be applied to personality research.

The Big Five Personality Traits and Brain Arousal in the Resting State.

Based on Eysenck's biopsychological trait theory, brain arousal has long been considered to explain individual differences in human personality. Yet, results from empirical studies remained inconclusive. However, most published results have been derived from small samples and, despite inherent limitations, EEG alpha power has usually served as an exclusive indicator for brain arousal. To overcome these problems, we here selected N = 468 individuals of the LIFE-Adult cohort and investigated the associations between the Big Five personality traits and brain arousal by using the validated EEG- and EOG-based analysis tool VIGALL. Our analyses revealed that participants who reported higher levels of extraversion and openness to experience, respectively, exhibited lower levels of brain arousal in the resting state. Bayesian and frequentist analysis results were especially convincing for openness to experience. Among the lower-order personality traits, we obtained the strongest evidence for neuroticism facet 'impulsivity' and reduced brain arousal. In line with this, both impulsivity and openness have previously been conceptualized as aspects of extraversion. We regard our findings as well in line with the postulations of Eysenck and consistent with the recently proposed 'arousal regulation model'. Our results also agree with meta-analytically derived effect sizes in the field of individual differences research, highlighting the need for large (collaborative) studies.

A new era for executive function research: On the transition from centralized to distributed executive functioning.

"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.

Cognitive Fatigue Predicts Cognitive Failure in Multiple Sclerosis Patients and Healthy Controls: A Case-Control Study.

OBJECTIVE: Fatigue and cognitive deficits are frequent symptoms of multiple sclerosis (MS). However, the exact nature of their co-occurrence is not fully understood. We sought to determine the impact of cognitive and physical fatigue on subjective cognitive deficits in MS patients and healthy controls. METHODS: Self-reports of fatigue (FSMC), depression (CES-D), cognitive deficits (CFQ), and personality traits (NEO-FFI, ANPS) among 30 MS inpatients and 30 healthy controls were analyzed using hierarchical regression models. The frequency of cognitive mistakes was used as the dependent variable and the extent of cognitive and physical fatigue as the independent variable. RESULTS: Cognitive fatigue was the only unique and significant predictor of cognitive mistakes in both groups, explaining 13.3% of additional variance in the MS group after correcting for age, mood, and physical fatigue. Physical fatigue had no significant impact on cognitive mistakes. While age had an impact on cognitive mistakes and depression in healthy controls, this association was not significant in MS patients. Depression was significantly correlated with cognitive mistakes and cognitive fatigue in MS patients. CONCLUSIONS: The interplay of cognitive fatigue and subjective cognitive impairment can be generalized, with the exception of the variables of age and depression, which were shown to have differing impacts on cognitive mistakes in MS patients and healthy controls, respectively. Cognitive fatigue was linked to cognitive mistakes even after correcting for overlapping items in MS patients only. Future research should further investigate the link between cognitive fatigue and attention lapses in daily life by using various objective assessments.

Molecular genetics in psychology and personality neuroscience: On candidate genes, genome wide scans, and new research strategies.

Despite the substantial heritability estimates for psychological traits, their precise genetic foundation from a molecular perspective remains elusive. We summarize findings and advances from more than twenty years of research into the molecular genetics of personality and other psychological traits. We describe how the candidate gene approach has - despite its appealing theoretical foundations - often (but not always) failed to point towards replicable associations between genetic polymorphisms and behavioral traits. The genome wide analysis approach on the contrary has become more fruitful in recent years and pointed towards reliable genetic associations. Results from genome wide scan studies (GWAS) are currently leveraged to explore gene-behavior associations through genetic correlation and polygenic score prediction which are important steps towards a precision medicine where treatment options are tailored to a patient's individual biology. But it is also true that future work needs to take a closer look at GWAS findings to link the growing list of statistical associations to biopsychological theory. We argue that research strategies from the candidate gene approach can be used to address these issues - given that necessary precautions are taken to avoid the problem of false-positive associations.

Specific and segregated changes to the functional connectome evoked by the processing of emotional faces: A task-based connectome study.

The functional connectome is organized into several separable intrinsic connectivity networks (ICNs) that are thought to be the building blocks of the mind. However, it is currently not well understood how these networks are engaged by emotionally salient information, and how such engagement fits into emotion theories. The current study assessed how ICNs respond during the processing of angry and fearful faces in a large sample (N = 843) and examined how connectivity changes relate to the ICNs. All ICNs were modulated by emotional faces and showed functional interactions, a finding which is in line with the "theory of constructed emotions" that assumes that basic emotion do not arise from separable ICNs but from their interplay. We further identified a set of brain regions whose connectivity changes during the tasks suggest a special role as "affective hubs" in the brain. While hubs were located in all ICNs, we observed high selectivity for the amygdala within the subcortical network, a finding which also fits into "primary emotion" theory. The topology of hubs corresponded closely to a set of brain regions that has been implicated in anxiety disorders, pointing towards a clinical relevance of the present findings. The present data are the most comprehensive mapping of connectome-wide changes in functionally connectivity evoked by an affective processing task thus far and support two competing views on how emotions are represented in the brain, suggesting that the connectome paradigm might help with unifying the two ideas.

What Does Our Personality Say About Our Dietary Choices? Insights on the Associations Between Dietary Habits, Primary Emotional Systems and the Dark Triad of Personality.

The awareness of the consequences of consuming animal products for the environment and one's own health has been growing in recent years. The aim of the present research project was to examine the relationship between individual differences in biologically rooted primary emotional systems arising from phylogenetically old brain areas and dietary habits including being a vegan/vegetarian or omnivore (Study 1). Additionally, the link between the Dark Triad personality traits and dietary habits was investigated (also Study 1). In Study 2 it was aimed to replicate the associations between the Dark Triad traits and dietary habits in a new sample. In total 1140 (Study 1) and 444 (Study 2) participants took part in the research project. The Affective Neuroscience Personality Scales (ANPS) were applied to assess individual differences in six primary emotional systems. The Short Dark Triad Scale (SD3) was administered to assess individual differences in Machiavellianism, psychopathy and narcissism. The eating style of participants was measured with the Eating Behavior Questionnaire (EBQ). Results of Study 1 demonstrated higher CARE, SADNESS and spirituality scores, and lower PLAY scores in vegans/vegetarians than in omnivores. However, after the sex of the participants was included in the model, the effect on CARE got weaker. Additionally, omnivores scored higher on Machiavellianism, however, this association disappeared when sex was added to the model. In Study 2, higher scores in Machiavellianism, narcissism and psychopathy were reported for the group of omnivores compared to vegans/vegetarians, however, those effects got weaker or disappeared after the sex of participants was added to the model. The present research project adds to the literature by investigating the ANPS model and the Dark Triad of personality in the context of eating style for the first time. The findings of these two studies might help to better understand how people following different types of diet, might differ in their personalities.

Ventral striatum and stuttering: Robust evidence from a case-control study applying DARTEL.

A prominent theory of developmental stuttering highlights (dys-)function of the basal ganglia (and in particular the ventral striatum) as a main neural mechanism behind this speech disorder. Although the theory is intriguing, studies on gray matter volume differences in the basal ganglia between people who stutter and control persons have reported heterogeneous findings, either showing more or less gray matter volume of the aforementioned brain structure across the brain's hemispheres. Moreover, some studies did not observe any differences at all. From today's perspective several of the earlier studies are rather underpowered and also used less powerful statistical approaches to investigate differences in brain structure between people who stutter and controls. Therefore, the present study contrasted a comparably larger sample of n = 36 people who stutter with n = 34 control persons and applied the state of the art DARTEL algorithm (Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra) to analyze the available brain data. In the present data set stuttering was associated with higher gray matter volume of the right caudate and putamen region of the basal ganglia in patients. Our observation strongly supports a recent finding reporting a larger nucleus accumbens in the right hemisphere in people who stutter when compared to control persons. The present findings are discussed in the context of both compensatory effects of the brain and putative therapeutic effects due to treatment of stuttering.