Although early visual experience is essential for the proper development of visual cortex, Striem-Amit et al. show that the underlying connectivity structure of retinotopic mapping is retained even in congenitally blind individuals. This basic organisational principle emerges independently of visual input and persists despite lifelong experience-dependent plasticity.
When assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients.
While ischemic stroke reflects focal damage determined by the affected vascular territory, clinical symptoms are often more complex and may be better explained by additional indirect effects of the focal lesion. Assumed to be structurally underpinned by anatomical connections, supporting evidence has been found using alterations in the functional connectivity of resting-state functional magnetic resonance imaging (fMRI) data in both sensorimotor and attention networks. To assess the generalizability of this phenomenon in a stroke population with heterogeneous lesions, we investigated the distal effects of lesions on a global level. Longitudinal resting-state fMRI scans were acquired at three consecutive time points, beginning during the acute phase (days 1, 7, and 90 post-stroke) in 12 patients after ischemic stroke. We found a preferential functional change in affected networks (i.e., networks containing lesions changed more during recovery when compared with unaffected networks). This change in connectivity was significantly correlated with clinical changes assessed with the National Institute of Health Stroke Scale. Our results provide evidence that the functional architecture of large-scale networks is critical to understanding the clinical effect and trajectory of post-stroke recovery.
Pathological gambling (PG) shares clinical characteristics with substance-use disorders and is thus discussed as a behavioral addiction. Recent neuroimaging studies on PG report functional changes in prefrontal structures and the mesolimbic reward system. While an imbalance between these structures has been related to addictive behavior, whether their dysfunction in PG is reflected in the interaction between them remains unclear. We addressed this question using functional connectivity resting-state fMRI in male subjects with PG and controls. Seed-based functional connectivity was computed using two regions-of-interest, based on the results of a previous voxel-based morphometry study, located in the prefrontal cortex and the mesolimbic reward system (right middle frontal gyrus and right ventral striatum). PG patients demonstrated increased connectivity from the right middle frontal gyrus to the right striatum as compared to controls, which was also positively correlated with nonplanning aspect of impulsiveness, smoking and craving scores in the PG group. Moreover, PG patients demonstrated decreased connectivity from the right middle frontal gyrus to other prefrontal areas as compared to controls. The right ventral striatum demonstrated increased connectivity to the right superior and middle frontal gyrus and left cerebellum in PG patients as compared to controls. The increased connectivity to the cerebellum was positively correlated with smoking in the PG group. Our results provide further evidence for alterations in functional connectivity in PG with increased connectivity between prefrontal regions and the reward system, similar to connectivity changes reported in substance use disorder.
In the acute phase of stroke, the use of imaging techniques aims to provide pathophysiological information concerning vascular patency, areas of hypoperfusion, and metabolic and structural damage. Based on such information, therapeutic decisions such as the administration of reperfusion medications are made. After the acute phase, brain plasticity and reorganization are the main mechanisms underlying functional recovery, and improvement is determined by functional adaptations of distributed brain networks mediated by connectivity.1 Accordingly, new therapeutic approaches, such as noninvasive brain stimulation, target the modulation of connectivity and network function. 2,3 At this stage, imaging-based biomarkers should reflect the status of cerebral networks. As the relevance of the network view of stroke becomes increasingly evident, 4 so does the usefulness of imaging techniques in the assessment of cerebral network function in clinical populations. Most notably is the use of resting-state functional MRI (rs-fMRI).rs-fMRI is a task-independent functional neuroimaging approach based on intrinsic low-frequency fluctuations (typically <0.1 Hz) in the blood oxygenation level-dependent (BOLD) signal. This signal can be used to compute the temporal correlations between spatially remote areas, termed: functional connectivity. In the healthy brain, functional connectivity is increased between areas that are part of the same functional network even in the absence of task. The resulting spatial patterns closely resemble the activation patterns identified during specific tasks, 5 and these networks are referred to as resting-state networks. 6 Thus, rs-fMRI provides an approach for detailed investigation of functional networks, as well as a more general method for assessing changes in intrinsic neuronal activity. Unlike task-based methods, measures of intrinsic functional connectivity allow for flexible post hoc analyses that probe multiple functional networks. Additionally, the minimal demands on the patient during the scanning session make the technique an optimal choice for clinical settings.rs-fMRI may offer the prospect of providing therapeutically useful information on both the focal vascular lesion and the connectivity-based reorganization and subsequent functional recovery. Here we provide an overview of recent applications of rs-fMRI to stroke diagnostics and prognostics and discuss future perspectives and considerations. We begin with methods used to characterize local alterations in acute stroke and proceed to describe studies of specific and general connectivity changes at various phases of the recovery process. For a detailed description of the studies reviewed here, see Table I in the online-only Data Supplement. Local Intrinsic BOLD Activity as a Measure of HypoperfusionCorrelation analyses based on the BOLD signal are thought to reflect neuronal synchronization. 7 However, the BOLD signal additionally contains information concerning local blood flow and oxygen consumption 8 and is, therefore, potentially...
Clinical diagnosis of disorders of consciousness (DOC) caused by brain injury poses great challenges since patients are often behaviorally unresponsive. A promising new approach towards objective DOC diagnosis may be offered by the analysis of ultra-slow (<0.1 Hz) spontaneous brain activity fluctuations measured with functional magnetic resonance imaging (fMRI) during the resting-state. Previous work has shown reduced functional connectivity within the “default network”, a subset of regions known to be deactivated during engaging tasks, which correlated with the degree of consciousness impairment. However, it remains unclear whether the breakdown of connectivity is restricted to the “default network”, and to what degree changes in functional connectivity can be observed at the single subject level. Here, we analyzed resting-state inter-hemispheric connectivity in three homotopic regions of interest, which could reliably be identified based on distinct anatomical landmarks, and were part of the “Extrinsic” (externally oriented, task positive) network (pre- and postcentral gyrus, and intraparietal sulcus). Resting-state fMRI data were acquired for a group of 11 healthy subjects and 8 DOC patients. At the group level, our results indicate decreased inter-hemispheric functional connectivity in subjects with impaired awareness as compared to subjects with intact awareness. Individual connectivity scores significantly correlated with the degree of consciousness. Furthermore, a single-case statistic indicated a significant deviation from the healthy sample in 5/8 patients. Importantly, of the three patients whose connectivity indices were comparable to the healthy sample, one was diagnosed as locked-in. Taken together, our results further highlight the clinical potential of resting-state connectivity analysis and might guide the way towards a connectivity measure complementing existing DOC diagnosis.
The functional organization of the brain can be represented as a low-dimensional space that reflects its macroscale hierarchy. The dimensions of this space, described as connectivity gradients, capture the similarity of areas' connections along a continuous space. Studying how pathological perturbations with known effects on functional connectivity affect these connectivity gradients provides support for their biological relevance. Previous work has shown that localized lesions cause widespread functional connectivity alterations in structurally intact areas, affecting a network of interconnected regions. By using acute stroke as a model of the effects of focal lesions on the connectome, we apply the connectivity gradient framework to depict how functional reorganization occurs throughout the brain, unrestricted by traditional definitions of functional network boundaries. We define a three-dimensional connectivity space template based on functional connectivity data from healthy controls. By projecting lesion locations into this space, we demonstrate that ischemic strokes result in dimension-specific alterations in functional connectivity over the first week after symptom onset. Specifically, changes in functional connectivity were captured along connectivity Gradients 1 and 3. The degree of functional connectivity change was associated with the distance from the lesion along these connectivity gradients (a measure of functional similarity) regardless of the anatomical distance from the lesion. Together, these results provide support for the biological validity of connectivity gradients and suggest a novel framework to characterize connectivity alterations after stroke.
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