Mohit H. Adhikari

Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

During the last several years, the focus of research on resting-state functional magnetic resonance imaging (fMRI) has shifted from the analysis of functional connectivity averaged over the duration of scanning sessions to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, we describe how they can be carried out and how to assess the performance of dFC measures, and we illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia and in human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used in assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology, however, are general and can be applied to any measure. The results are twofold. First, through simulations, we show that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions was evidence for dFC found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.

Hippocampal Sharp-Wave Ripples Influence Selective Activation of the Default Mode Network

Summary The default mode network (DMN) is a commonly observed resting-state network (RSN) that includes medial temporal, parietal, and prefrontal regions involved in episodic memory [1, 2, 3]. The behavioral relevance of endogenous DMN activity remains elusive, despite an emerging literature correlating resting fMRI fluctuations with memory performance [4, 5]—particularly in DMN regions [6, 7, 8]. Mechanistic support for the DMN’s role in memory consolidation might come from investigation of large deflections (sharp-waves) in the hippocampal local field potential that co-occur with high-frequency (>80 Hz) oscillations called ripples—both during sleep [9, 10] and awake deliberative periods [11, 12, 13]. Ripples are ideally suited for memory consolidation [14, 15], since the reactivation of hippocampal place cell ensembles occurs during ripples [16, 17, 18, 19]. Moreover, the number of ripples after learning predicts subsequent memory performance in rodents [20, 21, 22] and humans [23], whereas electrical stimulation of the hippocampus after learning interferes with memory consolidation [24, 25, 26]. A recent study in macaques showed diffuse fMRI neocortical activation and subcortical deactivation specifically after ripples [27]. Yet it is unclear whether ripples and other hippocampal neural events influence endogenous fluctuations in specific RSNs—like the DMN—unitarily. Here, we examine fMRI datasets from anesthetized monkeys with simultaneous hippocampal electrophysiology recordings, where we observe a dramatic increase in the DMN fMRI signal following ripples, but not following other hippocampal electrophysiological events. Crucially, we find increases in ongoing DMN activity after ripples, but not in other RSNs. Our results relate endogenous DMN fluctuations to hippocampal ripples, thereby linking network-level resting fMRI fluctuations with behaviorally relevant circuit-level neural dynamics.

Computational Modeling of Resting-State Activity Demonstrates Markers of Normalcy in Children with Prenatal or Perinatal Stroke

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere “take over” their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children.

Linking entropy at rest with the underlying structural connectivity in the healthy and lesioned brain

The brain is a network that mediates information processing through a wide range of states. The extent of state diversity is a reflection of the entropy of the network. Here we measured the entropy of brain regions (nodes) in empirical and modeled functional networks reconstructed from resting state fMRI to address the connection of entropy at rest with the underlying structure measured through diffusion spectrum imaging. Using 18 empirical and 18 modeled stroke networks, we also investigated the effect that focal lesions have on node entropy and information diffusion. Overall, positive correlations between node entropy and structure were observed, especially between node entropy and node strength in both empirical and modeled data. Although lesions were restricted to one hemisphere in all stroke patients, entropy reduction was not only present in nodes from the damaged hemisphere, but also in nodes from the contralesioned hemisphere, an effect replicated in modeled stroke networks. Globally, information diffusion was also affected in empirical and modeled strokes compared with healthy controls. This is the first study showing that artificial lesions affect local and global network aspects in very similar ways compared with empirical strokes, shedding new light into the functional nature of stroke.

Cell-Intrinsic Control of Interneuron Migration Drives Cortical Morphogenesis

Summary Interneurons navigate along multiple tangential paths to settle into appropriate cortical layers. They undergo a saltatory migration paced by intermittent nuclear jumps whose regulation relies on interplay between extracellular cues and genetic-encoded information. It remains unclear how cycles of pause and movement are coordinated at the molecular level. Post-translational modification of proteins contributes to cell migration regulation. The present study uncovers that carboxypeptidase 1, which promotes post-translational protein deglutamylation, controls the pausing of migrating cortical interneurons. Moreover, we demonstrate that pausing during migration attenuates movement simultaneity at the population level, thereby controlling the flow of interneurons invading the cortex. Interfering with the regulation of pausing not only affects the size of the cortical interneuron cohort but also impairs the generation of age-matched projection neurons of the upper layers.

Corrigendum to "Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?" [NeuroImage 127 (2016) 242-256].

a Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain b Department of Health Sciences and Technology, ETH Zurich, Switzerland c Department of Experimental Psychology, University of Oxford, United Kingdom d Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany e Instituci Catalana de la Recerca i Estudis Avanats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain

Mathematical modeling for resting state functional connectivity of cortical and sub-cortical networks

Keywords: LTS5 Reference EPFL-CONF-186680 Record created on 2013-05-22, modified on 2017-05-10