Deniz Vatansever

Default Mode Dynamics for Global Functional Integration

The default mode network (DMN) has been traditionally assumed to hinder behavioral performance in externally focused, goal-directed paradigms and to provide no active contribution to human cognition. However, recent evidence suggests greater DMN activity in an array of tasks, especially those that involve self-referential and memory-based processing. Although data that robustly demonstrate a comprehensive functional role for DMN remains relatively scarce, the global workspace framework, which implicates the DMN in global information integration for conscious processing, can potentially provide an explanation for the broad range of higher-order paradigms that report DMN involvement. We used graph theoretical measures to assess the contribution of the DMN to global functional connectivity dynamics in 22 healthy volunteers during an fMRI-based n-back working-memory paradigm with parametric increases in difficulty. Our predominant finding is that brain modularity decreases with greater task demands, thus adapting a more global workspace configuration, in direct relation to increases in reaction times to correct responses. Flexible default mode regions dynamically switch community memberships and display significant changes in their nodal participation coefficient and strength, which may reflect the observed whole-brain changes in functional connectivity architecture. These findings have important implications for our understanding of healthy brain function, as they suggest a central role for the DMN in higher cognitive processing. SIGNIFICANCE STATEMENT The default mode network (DMN) has been shown to increase its activity during the absence of external stimulation, and hence was historically assumed to disengage during goal-directed tasks. Recent evidence, however, implicates the DMN in self-referential and memory-based processing. We provide robust evidence for this networks active contribution to working memory by revealing dynamic reconfiguration in its interactions with other networks and offer an explanation within the global workspace theoretical framework. These promising findings may help redefine our understanding of the exact DMN role in human cognition.

Cortical Overgrowth in Fetuses With Isolated Ventriculomegaly

Mild cerebral ventricular enlargement is associated with schizophrenia, autism, epilepsy, and attention-deficit/hyperactivity disorder. Fetal ventriculomegaly is the most common central nervous system (CNS) abnormality affecting 1% of fetuses and is associated with cognitive, language, and behavioral impairments in childhood. Neurodevelopmental outcome is partially predictable by the 2-dimensional size of the ventricles in the absence of other abnormalities. We hypothesized that isolated fetal ventriculomegaly is a marker of altered brain development characterized by relative overgrowth and aimed to quantify brain growth using volumetric magnetic resonance imaging (MRI) in fetuses with isolated ventriculomegaly. Fetal brain MRI (1.5 T) was performed in 60 normal fetuses and 65 with isolated ventriculomegaly, across a gestational age range of 22-38 weeks. Volumetric analysis of the ventricles and supratentorial brain structures was performed on 3-dimensional reconstructed datasets. Fetuses with isolated ventriculomegaly had increased brain parenchyma volumes when compared with the control cohort (9.6%, P < 0.0001) with enlargement restricted to the cortical gray matter (17.2%, P = 0.002). The extracerebral cerebrospinal fluid and third and fourth ventricles were also enlarged. White matter, basal ganglia, and thalamic volumes were not significantly different between cohorts. The presence of relative cortical overgrowth in fetuses with ventriculomegaly may represent the neurobiological substrate for cognitive, language, and behavioral deficits in these children.

Multidimensional Analysis of Fetal Posterior Fossa in Health and Disease

Fetal magnetic resonance imaging (MRI) is now routinely used to further investigate cerebellar malformations detected with ultrasound. However, the lack of 2D and 3D biometrics in the current literature hinders the detailed characterisation and classification of cerebellar anomalies. The main objectives of this fetal neuroimaging study were to provide normal posterior fossa growth trajectories during the second and third trimesters of pregnancy via semi-automatic segmentation of reconstructed fetal brain MR images and to assess common cerebellar malformations in comparison with the reference data. Using a 1.5-T MRI scanner, 143 MR images were obtained from 79 normal control and 53 fetuses with posterior fossa abnormalities that were grouped according to the severity of diagnosis on visual MRI inspections. All quantifications were performed on volumetric datasets, and supplemental outcome information was collected from the surviving infants. Normal growth trajectories of total brain, cerebellar, vermis, pons and fourth ventricle volumes showed significant correlations with 2D measurements and increased in second-order polynomial trends across gestation (Pearson r, p < 0.05). Comparison of normal controls to five abnormal cerebellum subgroups depicted significant alterations in volumes that could not be detected exclusively with 2D analysis (MANCOVA, p < 0.05). There were 15 terminations of pregnancy, 8 neonatal deaths, and a spectrum of genetic and neurodevelopmental outcomes in the assessed 24 children with cerebellar abnormalities. The given posterior fossa biometrics enhance the delineation of normal and abnormal cerebellar phenotypes on fetal MRI and confirm the advantages of utilizing advanced neuroimaging tools in clinical fetal research.

Atomoxetine Enhances Connectivity of Prefrontal Networks in Parkinson’s Disease

Cognitive impairment is common in Parkinson’s disease (PD), but often not improved by dopaminergic treatment. New treatment strategies targeting other neurotransmitter deficits are therefore of growing interest. Imaging the brain at rest (‘task-free’) provides the opportunity to examine the impact of a candidate drug on many of the brain networks that underpin cognition, while minimizing task-related performance confounds. We test this approach using atomoxetine, a selective noradrenaline reuptake inhibitor that modulates the prefrontal cortical activity and can facilitate some executive functions and response inhibition. Thirty-three patients with idiopathic PD underwent task-free fMRI. Patients were scanned twice in a double-blind, placebo-controlled crossover design, following either placebo or 40-mg oral atomoxetine. Seventy-six controls were scanned once without medication to provide normative data. Seed-based correlation analyses were used to measure changes in functional connectivity, with the right inferior frontal gyrus (IFG) a critical region for executive function. Patients on placebo had reduced connectivity relative to controls from right IFG to dorsal anterior cingulate cortex and to left IFG and dorsolateral prefrontal cortex. Atomoxetine increased connectivity from the right IFG to the dorsal anterior cingulate. In addition, the atomoxetine-induced change in connectivity from right IFG to dorsolateral prefrontal cortex was proportional to the change in verbal fluency, a simple index of executive function. The results support the hypothesis that atomoxetine may restore prefrontal networks related to executive functions. We suggest that task-free imaging can support translational pharmacological studies of new drug therapies and provide evidence for engagement of the relevant neurocognitive systems.

Tau burden and the functional connectome in Alzheimer’s disease and progressive supranuclear palsy

Prion-like, trans-neuronal spread of tau pathology in humans is controversial. By evaluating tau burden and functional connectivity in living patients, Cope et al. demonstrate relationships consistent with this in Alzheimers disease but not progressive supranuclear palsy. Tau distribution in the latter is better explained by metabolic demand and trophic support.

Angular default mode network connectivity across working memory load.

Initially identified during no‐task, baseline conditions, it has now been suggested that the default mode network (DMN) engages during a variety of working memory paradigms through its flexible interactions with other large‐scale brain networks. Nevertheless, its contribution to whole‐brain connectivity dynamics across increasing working memory load has not been explicitly assessed. The aim of our study was to determine which DMN hubs relate to working memory task performance during an fMRI‐based n‐back paradigm with parametric increases in difficulty. Using a voxel‐wise metric, termed the intrinsic connectivity contrast (ICC), we found that the bilateral angular gyri (core DMN hubs) displayed the greatest change in global connectivity across three levels of n‐back task load. Subsequent seed‐based functional connectivity analysis revealed that the angular DMN regions robustly interact with other large‐scale brain networks, suggesting a potential involvement in the global integration of information. Further support for this hypothesis comes from the significant correlations we found between angular gyri connectivity and reaction times to correct responses. The implication from our study is that the DMN is actively involved during the n‐back task and thus plays an important role related to working memory, with its core angular regions contributing to the changes in global brain connectivity in response to increasing environmental demands. Hum Brain Mapp 38:41–52, 2017.

Varieties of semantic cognition revealed through simultaneous decomposition of intrinsic brain connectivity and behaviour

Contemporary theories assume that semantic cognition emerges from a neural architecture in which different component processes are combined to produce aspects of conceptual thought and behaviour. In addition to the state-level, momentary variation in brain connectivity, individuals may also differ in their propensity to generate particular configurations of such components, and these trait-level differences may relate to individual differences in semantic cognition. We tested this view by exploring how variation in intrinsic brain functional connectivity between semantic nodes in fMRI was related to performance on a battery of semantic tasks in 154 healthy participants. Through simultaneous decomposition of brain functional connectivity and semantic task performance, we identified distinct components of semantic cognition at rest. In a subsequent validation step, these data-driven components demonstrated explanatory power for neural responses in an fMRI-based semantic localiser task and variation in self-generated thoughts during the resting-state scan. Our findings showed that good performance on harder semantic tasks was associated with relative segregation at rest between frontal brain regions implicated in controlled semantic retrieval and the default mode network. Poor performance on easier tasks was linked to greater coupling between the same frontal regions and the anterior temporal lobe; a pattern associated with deliberate, verbal thematic thoughts at rest. We also identified components that related to qualities of semantic cognition: relatively good performance on pictorial semantic tasks was associated with greater separation of angular gyrus from frontal control sites and greater integration with posterior cingulate and anterior temporal cortex. In contrast, good speech production was linked to the separation of angular gyrus, posterior cingulate and temporal lobe regions. Together these data show that quantitative and qualitative variation in semantic cognition across individuals emerges from variations in the interaction of nodes within distinct functional brain networks.

Cognitive Flexibility: A Default Network and Basal Ganglia Connectivity Perspective

The intra/extradimensional set-shifting task (IED) provides a reliable assessment of cognitive flexibility, the shifting of attention to select behaviorally relevant stimuli in a given context. Impairments in this domain were previously reported in patients with altered neurotransmitter systems such as schizophrenia and Parkinsons disease. Consequently, corticostriatal connections were implicated in the mediation of this function. In addition, parts of the default mode network (DMN), namely the medial prefrontal and posterior cingulate/precuneus cortices, are also being progressively described in association with set-shifting paradigms. Nevertheless, a definitive link between cognitive flexibility and DMN connectivity remains to be established. To this end, we related resting state functional magnetic resonance imaging (fMRI)-based functional connectivity of DMN with IED task performance in a healthy population, measured outside the scanner. The results demonstrated that greater posterior cingulate cortex/precuneus (DMN) connectivity with the ventromedial striatopallidum at rest correlated with fewer total adjusted errors on the IED task. This finding points to a relationship between DMN and basal ganglia connectivity for cognitive flexibility, further highlighting this networks potential role in adaptive human cognition.

Default mode contributions to automated information processing

Significance In addition to dealing with variable demands of the environment in everyday life, we are continuously faced with routine, predictable challenges that require fast and effective responses. In an fMRI-based cognitive flexibility task, we show greater activity/connectivity centered on the default mode network during such automated decision-making under predictable environmental demands. Furthermore, we report on a significant correlation between this network and hippocampal connectivity and individual differences in the participants’ ability to make automated, fast, and accurate responses. Together, these results suggest an “autopilot” role for this network that may have important theoretical implications for our understanding of healthy brain processing in meeting worldly demands. Concurrent with mental processes that require rigorous computation and control, a series of automated decisions and actions govern our daily lives, providing efficient and adaptive responses to environmental demands. Using a cognitive flexibility task, we show that a set of brain regions collectively known as the default mode network plays a crucial role in such “autopilot” behavior, i.e., when rapidly selecting appropriate responses under predictable behavioral contexts. While applying learned rules, the default mode network shows both greater activity and connectivity. Furthermore, functional interactions between this network and hippocampal and parahippocampal areas as well as primary visual cortex correlate with the speed of accurate responses. These findings indicate a memory-based “autopilot role” for the default mode network, which may have important implications for our current understanding of healthy and adaptive brain processing.

Graph-Based Label Propagation in Fetal Brain MR Images

Segmentation of neonatal and fetal brain MR images is a challenging task due to vast differences in shape and appearance across age and across subjects. Expert priors for atlas-based segmentation are often only available for a subset of the population, leading to a reduction in accuracy for images dissimilar from the atlas set. To alleviate the effects of limited prior information on atlas-based segmentation, we present a novel semi-supervised learning framework where labels are propagated among both atlas and test images while modelling the confidence of propagated information. The method relies on a voxel-wise graph interconnecting similar regions in all images based on a patch similarity measure. By iteratively allowing information flow from voxels with high confidence to voxels with lower confidence, segmentations in test images with low similarity to the atlas set can be improved. The method was evaluated on 70 fetal brain MR images of subjects at 22–38 weeks gestational age. Particularly for test populations dissimilar from the atlas population, the proposed method outperformed state-of-the-art patch-based segmentation.