Murat Demirtas

Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder

Resting‐state fMRI (RS‐fMRI) has become a useful tool to investigate the connectivity structure of mental health disorders. In the case of major depressive disorder (MDD), recent studies regarding the RS‐fMRI have found abnormal connectivity in several regions of the brain, particularly in the default mode network (DMN). Thus, the relevance of the DMN to self‐referential thoughts and ruminations has made the use of the resting‐state approach particularly important for MDD. The majority of such research has relied on the grand averaged functional connectivity measures based on the temporal correlations between the BOLD time series of various brain regions. We, in our study, investigated the variations in the functional connectivity over time at global and local level using RS‐fMRI BOLD time series of 27 MDD patients and 27 healthy control subjects. We found that global synchronization and temporal stability were significantly increased in the MDD patients. Furthermore, the participants with MDD showed significantly increased overall average (static) functional connectivity (sFC) but decreased variability of functional connectivity (vFC) within specific networks. Static FC increased to predominance among the regions pertaining to the default mode network (DMN), while the decreased variability of FC was observed in the connections between the DMN and the frontoparietal network. Hum Brain Mapp 37:2918–2930, 2016.

A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease

Alzheimers disease (AD) is the most common dementia with dramatic consequences. The research in structural and functional neuroimaging showed altered brain connectivity in AD. In this study, we investigated the whole-brain resting state functional connectivity (FC) of the subjects with preclinical Alzheimers disease (PAD), mild cognitive impairment due to AD (MCI) and mild dementia due to Alzheimers disease (AD), the impact of APOE4 carriership, as well as in relation to variations in core AD CSF biomarkers. The synchronization in the whole-brain was monotonously decreasing during the course of the disease progression. Furthermore, in AD patients we found widespread significant decreases in functional connectivity (FC) strengths particularly in the brain regions with high global connectivity. We employed a whole-brain computational modeling approach to study the mechanisms underlying these alterations. To characterize the causal interactions between brain regions, we estimated the effective connectivity (EC) in the model. We found that the significant EC differences in AD were primarily located in left temporal lobe. Then, we systematically manipulated the underlying dynamics of the model to investigate simulated changes in FC based on the healthy control subjects. Furthermore, we found distinct patterns involving CSF biomarkers of amyloid-beta (Aβ1 − 42) total tau (t-tau) and phosphorylated tau (p-tau). CSF Aβ1 − 42 was associated to the contrast between healthy control subjects and clinical groups. Nevertheless, tau CSF biomarkers were associated to the variability in whole-brain synchronization and sensory integration regions. These associations were robust across clinical groups, unlike the associations that were found for CSF Aβ1 − 42. APOE4 carriership showed no significant correlations with the connectivity measures.

Hierarchy of transcriptomic specialization across human cortex captured by myelin map topography

Hierarchy provides a unifying principle for the macroscale organization of anatomical and functional properties across primate cortex, yet the microscale bases of specialization across human cortex are poorly understood. Cortical hierarchy is conventionally informed by invasive measurements of long-range projections, creating the need for a principled proxy measure of hierarchy in humans. Moreover, cortex exhibits marked interareal variation in patterns of gene expression, yet organizing principles of its transcriptional architecture remain unclear. We hypothesized that functional specialization of human cortical microcircuitry involves hierarchical gradients of gene expression. We found that a noninvasive neuroimaging measure, the MRI-derived myelin map, reliably indexes hierarchy and closely resembles the dominant pattern of transcriptomic variation across human cortex. We found strong hierarchical gradients in expression profiles of genes related to microcircuit function and neuropsychiatric disorders. Our findings suggest that hierarchy defines an axis shared by the transcriptomic and anatomical architectures of human cortex, and that hierarchical gradients of microscale properties contribute to macroscale specialization of cortical function.

Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography

Hierarchy provides a unifying principle for the macroscale organization of anatomical and functional properties across primate cortex, yet microscale bases of specialization across human cortex are poorly understood. Anatomical hierarchy is conventionally informed by invasive tract-tracing measurements, creating a need for a principled proxy measure in humans. Moreover, cortex exhibits marked interareal variation in gene expression, yet organizing principles of cortical transcription remain unclear. We hypothesized that specialization of cortical microcircuitry involves hierarchical gradients of gene expression. We found that a noninvasive neuroimaging measure—MRI-derived T1-weighted/T2-weighted (T1w/T2w) mapping—reliably indexes anatomical hierarchy, and it captures the dominant pattern of transcriptional variation across human cortex. We found hierarchical gradients in expression profiles of genes related to microcircuit function, consistent with monkey microanatomy, and implicated in neuropsychiatric disorders. Our findings identify a hierarchical axis linking cortical transcription and anatomy, along which gradients of microscale properties may contribute to the macroscale specialization of cortical function.Burt et al. analyze patterns of gene expression across human cortex and show expression primarily varies along a sensory-association hierarchy captured by noninvasive neuroimaging, suggesting an organizing principle for microcircuit specialization.

Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics

The large-scale organization of dynamical neural activity across cortex emerges through long-range interactions among local circuits. We hypothesized that large-scale dynamics are also shaped by heterogeneity of intrinsic local properties across cortical areas. One key axis along which microcircuit properties are specialized relates to hierarchical levels of cortical organization. We developed a large-scale dynamical circuit model of human cortex that incorporates heterogeneity of local synaptic strengths, following a hierarchical axis inferred from MRI-derived T1w/T2w mapping, and fit the model using multimodal neuroimaging data. We found that incorporating hierarchical heterogeneity substantially improves the model fit to fMRI-measured resting-state functional connectivity and captures sensory-association organization of multiple fMRI features. The model predicts hierarchically organized high-frequency spectral power, which we tested with resting-state magnetoencephalography. These findings suggest circuit-level mechanisms linking spatiotemporal levels of analysis and highlight the importance of local properties and their hierarchical specialization on the large-scale organization of human cortical dynamics.

Chapter 4 – Computational Models of Dysconnectivity in Large-Scale Resting-State Networks

Abstract Brain activity, on every scale, spontaneously fluctuates, thereby exhibiting complex, dynamic interactions that manifest rich synchronization patterns. In the past decade, many studies have advanced our understanding of the mechanisms behind the dynamic interactions within the brain through the basis of its structural and functional connectivity (FC) structures. Moreover, there is a tremendous effort to unveil the role that these interactions play in psychiatric disorders. Specifically, neuroimaging techniques such as functional magnetic resonance imaging have provided robust evidence regarding large-scale coordinated fluctuations in the brain at rest (i.e., the subject is idled by her own pace, with no explicit physical or mental task being given). Within a short time, scientists around the world have reported complex spatiotemporal connectivity structures (i.e., resting-state networks; RSNs) appearing in resting-state neuroimaging recordings. In parallel, many studies have shown the clinical significance of RSNs in psychiatric disorders. However, the connectivity patterns observed through the neuroimaging modalities are highly complex and interdependent. This makes the interpretation of the results nontrivial; hence the current approaches fail to provide a mechanistic understanding of brain function. Computational modeling studies first addressed the link between anatomical and FC. The success of these models has inspired the computational neuroscience community to develop adequate models that can capture empirically observed dynamic interactions in the brain. Despite being its infancy, these models are key to tackle the complexity of the brain dynamics and to unveil the hidden mechanisms. In this chapter, we provide an overview of how computational modeling approaches might help us to understand brain dynamics at the macroscopic scale.

Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry

Noninvasive neuroimaging has revolutionized the study of the organization of the human brain and how its structure and function are altered in psychiatric disorders. A critical explanatory gap lies in our mechanistic understanding of how systems-level neuroimaging biomarkers emerge from underlying synaptic-level perturbations associated with a disease state. We describe an emerging computational psychiatry approach leveraging biophysically based computational models of large-scale brain dynamics and their potential integration with clinical and pharmacological neuroimaging. In particular, we focus on neural circuit models, which describe how patterns of functional connectivity observed in resting-state functional magnetic resonance imaging emerge from neural dynamics shaped by inter-areal interactions through underlying structural connectivity defining long-range projections. We highlight the importance of local circuit physiological dynamics, in combination with structural connectivity, in shaping the emergent functional connectivity. Furthermore, heterogeneity of local circuit properties across brain areas, which impacts large-scale dynamics, may be critical for modeling whole-brain phenomena and alterations in psychiatric disorders and pharmacological manipulation. Finally, we discuss important directions for future model development and biophysical extensions, which will expand their utility to link clinical neuroimaging to neurobiological mechanisms.

Distinct modes of functional connectivity induced by movie-watching

&NA; A fundamental question in systems neuroscience is how endogenous neuronal activity self‐organizes during particular brain states. Recent neuroimaging studies have demonstrated systematic relationships between resting‐state and task‐induced functional connectivity (FC). In particular, continuous task studies, such as movie watching, speak to alterations in coupling among cortical regions and enhanced fluctuations in FC compared to the resting‐state. This suggests that FC may reflect systematic and large‐scale reorganization of functionally integrated responses while subjects are watching movies. In this study, we characterized fluctuations in FC during resting‐state and movie‐watching conditions. We found that the FC patterns induced systematically by movie‐watching can be explained with a single principal component. These condition‐specific FC fluctuations overlapped with inter‐subject synchronization patterns in occipital and temporal brain regions. However, unlike inter‐subject synchronization, condition‐specific FC patterns were characterized by increased correlations within frontal brain regions and reduced correlations between frontal‐parietal brain regions. We investigated these condition‐specific functional variations as a shorter time scale, using time‐resolved FC. The time‐resolved FC showed condition‐specificity over time; notably when subjects watched both the same and different movies. To explain self‐organisation of global FC through the alterations in local dynamics, we used a large‐scale computational model. We found that condition‐specific reorganization of FC could be explained by local changes that engendered changes in FC among higher‐order association regions, mainly in frontal and parietal cortices. HighlightsThe variations of functional connectivity during movie‐watching condition are explained by a single principal component.The topography of condition‐specific principal component is similar to inter‐subject synchronization in occipital and temporal brain regions, but it exhibits distinct patterns expressed in frontal brain regions.Time‐resolved functional connectivity shows that the condition‐specific functional states are continuous across time.A whole‐brain computational model shows that the changes in local dynamical properties in higher‐order association regions can explain the condition‐specific changes in FC.

Distinct Functional Connectivity Mode during Viewing Natural Scenes Revealed by Principal Component Analysis

A fundamental question in systems neuroscience is how endogenous neuronal activity self-organizes during particular brain states. Recent neuroimaging studies have revealed systematic relationships between resting-state and task-induced functional connectivity (FC). In particular, continuous task studies, such as movie watching, speak to alterations in coupling among cortical regions and enhanced fluctuations in FC compared to resting-state. This suggests that FC may reflect systematic and large-scale reorganization of functionally integrated responses while subjects are watching movies. In this study, we characterized fluctuations in FC during resting-state and movie-watching conditions. We found that the FC patterns induced systematically by movie-watching can be explained with a single principal component. These condition-specific FC fluctuations overlapped with inter-subject synchronization patterns in occipital and temporal brain regions. However, unlike inter-subject synchronization, condition-specific FC patterns contained increased correlations within frontal brain regions and reduced correlations between frontal-parietal brain regions. We investigated the condition-specific functional variations as a shorter time scale, using time-resolved FC. The time-resolved FC showed condition-specificity over time, notably when subjects were also watching the same and different movie scenes. To explain the self-organisation of whole-brain FC through the alterations in local dynamics, we used a large-scale computational model. We found that the condition-specific reorganization of FC could be explained by local changes that engendered changes in FC among higher-order association regions, mainly in frontal parietal cortices.A fundamental question in systems neuroscience is how spontaneous activity at rest is reorganized during task performance. Recent studies suggest a strong relationship between resting and task FC. Furthermore, the relationship between resting and task FC has been shown to reflect individual differences. Particularly, various studies have demonstrated that the FC has higher reliability and provides enhanced detection of individual differences while viewing natural scenes. Although the large-scale organization of FC during rest and movie-viewing conditions have been well studied in relation to individual variations, the re-organization of FC during viewing natural scenes have not been studied in depth. In this study, we used principal component analysis on FC during rest and movie-viewing condition to characterize the dimensionality of FC patterns across conditions and subjects. We found that the variations in FC patterns related to viewing natural scenes can be explained by a single component, which enables identification of the task over subjects with 100% accuracy. We showed that the FC mode associated to viewing natural scenes better reflects individual variations. Furthermore, we investigated the signatures of movie-viewing-specific functional modes in dynamic FC based on phase-locking values between brain regions. We found that the movie-specific functional mode is persistent across time; suggesting the emergence of a stable processing mode. To explain the reorganization of whole-brain FC through the changes in local dynamics, we appeal to a large-scale computational model. This modelling suggested that the reorganization of whole-brain FC is associated to the interaction between frontal-parietal and frontal-temporal activation patterns.

Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

Resting-state functional magnetic resonance imaging and diffusion weight imaging became a conventional tool to study brain connectivity in healthy and diseased individuals. However, both techniques provide indirect measures of brain connectivity leading to controversies on their interpretation. Among these controversies, interpretation of anti-correlated functional connections and global average signal is a major challenge for the field. In this paper, we used dynamic functional connectivity to calculate the probability of anti-correlations between brain regions. The brain regions forming task-positive and task-negative networks showed high anti-correlation probabilities. The fluctuations in anti-correlation probabilities were significantly correlated with those in global average signal and functional connectivity. We investigated the mechanisms behind these fluctuations using whole-brain computational modeling approach. We found that the underlying effective connectivity and intrinsic noise reflect the static spatiotemporal patterns, whereas the hemodynamic response function is the key factor defining the fluctuations in functional connectivity and anti-correlations. Furthermore, we illustrated the clinical implications of these findings on a group of bipolar disorder patients suffering a depressive relapse (BPD).