Typical and Atypical Development of Functional Human Brain Networks: Insights from Resting-State fMRI

Over the past several decades, structural MRI studies have provided remarkable insights into human brain development by revealing the trajectory of gray and white matter maturation from childhood to adolescence and adulthood. In parallel, functional MRI studies have demonstrated changes in brain activation patterns accompanying cognitive development. Despite these advances, studying the maturation of functional brain networks underlying brain development continues to present unique scientific and methodological challenges. Resting-state fMRI (rsfMRI) has emerged as a novel method for investigating the development of large-scale functional brain networks in infants and young children. We review existing rsfMRI developmental studies and discuss how this method has begun to make significant contributions to our understanding of maturing brain organization. In particular, rsfMRI has been used to complement studies in other modalities investigating the emergence of functional segregation and integration across short and long-range connections spanning the entire brain. We show that rsfMRI studies help to clarify and reveal important principles of functional brain development, including a shift from diffuse to focal activation patterns, and simultaneous pruning of local connectivity and strengthening of long-range connectivity with age. The insights gained from these studies also shed light on potentially disrupted functional networks underlying atypical cognitive development associated with neurodevelopmental disorders. We conclude by identifying critical gaps in the current literature, discussing methodological issues, and suggesting avenues for future research

Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications

Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163

Studying Brain Organization via Spontaneous fMRI Signal

In recent years, some substantial advances in understanding human (and nonhuman) brain organization have emerged from a relatively unusual approach: the observation of spontaneous activity, and correlated patterns in spontaneous activity, in the “resting” brain. Most commonly, spontaneous neural activity is measured indirectly via fMRI signal in subjects who are lying quietly in the scanner, the so-called “resting state.” This Primer introduces the fMRI-based study of spontaneous brain activity, some of the methodological issues active in the field, and some ways in which resting-state fMRI has been used to delineate aspects of area-level and supra-areal brain organization

Brain enhancement through cognitive training: A new insight from brain connectome

Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function

Building an adaptive brain across development: targets for neurorehabilitation must begin in infancy

Much progress has been made toward behavioural and pharmacological intervention in intellectual disability, which was once thought too difficult to treat. Down syndrome research has shown rapid advances, and clinical trials are currently underway, with more on the horizon. Here, we review the literature on the emergent profile of cognitive development in Down syndrome, emphasizing that treatment approaches must consider how some “end state” impairments, such as language deficits, may develop from early alterations in neural systems beginning in infancy. Specifically, we highlight evidence suggesting that there are pre- and early postnatal alterations in brain structure and function in Down syndrome, resulting in disturbed network function across development. We stress that these early alterations are likely amplified by Alzheimer’s disease progression and poor sleep. Focusing on three network hubs (prefrontal cortex, hippocampus, and cerebellum), we discuss how these regions may relate to evolving deficits in cognitive function in individuals with Down syndrome, and to their language profile in particular

MR connectomics: a conceptual framework for studying the developing brain

The combination of advanced neuroimaging techniques and major developments in complex network science, have given birth to a new framework for studying the brain: “connectomics.” This framework provides the ability to describe and study the brain as a dynamic network and to explore how the coordination and integration of information processing may occur. In recent years this framework has been used to investigate the developing brain and has shed light on many dynamic changes occurring from infancy through adulthood. The aim of this article is to review this work and to discuss what we have learned from it. We will also use this body of work to highlight key technical aspects that are necessary in general for successful connectome analysis using today's advanced neuroimaging techniques. We look to identify current limitations of such approaches, what can be improved, and how these points generalize to other topics in connectome research

The Problem of Mental Action

In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference

A quantitative analysis of thalamocortical white matter development in benign childhood epilepsy with centro-temporal spikes (BECTS)

BACKGROUND: A number of epilepsy syndromes are characterized by sleep-activated epileptiform discharges, however drivers of this process are not well understood. Previous research has found that thalamic injury in early life may increase the odds of sleep-activated spikes. Benign childhood epilepsy with centrotemporal spikes (BECTS) is among the most common pediatric-onset epilepsy syndromes, characterized by sleep-potentiated spike activity, a focal sensorimotor seizure semiology, and deficits in language, attention, and behavioral functioning. Though ictal and interictal electro-clinical activity resolves during mid-adolescence, adverse psychosocial outcomes may persist. Previous findings from monozygotic twin and neuroimaging studies suggest a multifactorial pattern of disease and raise suspicion for structural changes in thalamocortical connectivity focal to the seizure onset zone, though this has not been explored. OBJECTIVE: This research aims to (1) assess white matter differences in focal thalamocortical connectivity between BECTS cases and healthy controls using validated probabilistic tractography methods, (2) assess the association between spike burden and white matter connectivity focal to the seizure onset zone, and (3) evaluate longitudinal changes in thalamocortical connectivity across four cases. METHODS: 42 subjects ages 6-15 years were recruited between November 2015 and February 2018, including 23 BECTS cases and 19 healthy controls. Subjects underwent 3 Tesla structural and diffusion-weighted magnetic resonance imaging (2mm x 2mm x 2mm) with 64 gradient directions (b-value=2000) and 72 electrode sleep-deprived electroencephalographic (EEG) recordings. Seed and target regions of interest (ROIs) were created within each hemisphere using the Desikan-Killiany atlas, with the thalamus set as a seed ROI, and SOZ cortex and non-SOZ (NSOZ) cortex as target ROIs. Probabilistic tractography was executed using PROBTRACKX2 with 500 streamlines per seed voxel, 0.5 millimeter steps, and a curvature threshold of 0.2. All streamlines reaching the target ROI were summed and normalized by seed voxel count. Results for BECTS and healthy controls were plotted by age. The slope of thalamocortical connectivity versus age was computed for each group and compared between groups using nonparametric bootstrap analysis. Additionally, the association between SOZ connectivity and spike burden was assessed in a subgroup analysis using a linear regression model, controlling for age. RESULTS: A significant difference in the developmental trajectory of thalamocortical connectivity to the SOZ in BECTS cases compared to healthy controls was found (p=0.014), where the increase in connectivity with age observed in healthy controls was not present in BECTS children. These results did not extend to NSOZ thalamocortical connections (p=0.192). Longitudinal results support these observations, where all BECTS cases who underwent repeat imaging (N=4) showed a decrease in thalamocortical connectivity to the SOZ over the follow-up period. No relationship was found between thalamocortical connectivity and spike burden (p=0.840). CONCLUSIONS: These findings suggest that children with BECTS show subtle alterations in thalamocortical white matter development focal to the seizure onset zone. Thalamocortical connectivity to the SOZ does not appear to directly mediate non-REM sleep spike potentiation in BECTS. Limitations of this study include the potential for selection bias and limited power to detect sample differences. Additional research is needed to further characterize thalamocortical network changes and electrographic and neuropsychological correlates

The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity

The Developing Human Connectome Project (dHCP) is an Open Science project which provides the first large sample of neonatal functional MRI (fMRI) data with high temporal and spatial resolution. This data enables mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organisation in humans. Here, we characterise in unprecedented detail the maturation and integrity of resting-state networks (RSNs) at term-equivalent age in 337 infants (including 65 born preterm). First, we applied group independent component analysis (ICA) to define 11 RSNs in term-born infants scanned at 43.5-44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37-43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder which occur frequently in preterm children. Overall, we find a robust, modular, symmetrical functional brain organisation at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher-order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity

Impaired Resting-State Functional Integrations within Default Mode Network of Generalized Tonic-Clonic Seizures Epilepsy

Generalized tonic-clonic seizures (GTCS) are characterized by unresponsiveness and convulsions, which cause complete loss of consciousness. Many recent studies have found that the ictal alterations in brain activity of the GTCS epilepsy patients are focally involved in some brain regions, including thalamus, upper brainstem, medial prefrontal cortex, posterior midbrain regions, and lateral parietal cortex. Notably, many of these affected brain regions are the same and overlap considerably with the components of the so-called default mode network (DMN). Here, we hypothesize that the brain activity of the DMN of the GTCS epilepsy patients are different from normal controls, even in the resting state. To test this hypothesis, we compared the DMN of the GTCS epilepsy patients and the controls using the resting state functional magnetic resonance imaging. Thirteen brain areas in the DMN were extracted, and a complete undirected weighted graph was used to model the DMN for each participant. When directly comparing the edges of the graph, we found significant decreased functional connectivities within the DMN of the GTCS epilepsy patients comparing to the controls. As for the nodes of the graph, we found that the degree of some brain areas within the DMN was significantly reduced in the GTCS epilepsy patients, including the anterior medial prefrontal cortex, the bilateral superior frontal cortex, and the posterior cingulate cortex. Then we investigated into possible mechanisms of how GTCS epilepsy could cause the reduction of the functional integrations of DMN. We suggested the damaged functional integrations of the DMN in the GTCS epilepsy patients even during the resting state, which could help to understand the neural correlations of the impaired consciousness of GTCS epilepsy patients