Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including ...schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.
Objective
Amygdala‐based network dysfunction has been found to be centrally implicated in major depressive disorder (MDD). However, relatively little is known about how different forms of effective ...or cognitive dysfunction are modulated in MDD. Therefore, in the current study, we aimed to examine the alteration of amygdala subregional networks in adult patients with MDD to explore whether different parts of the amygdala that are functionally connected to different regions contribute differently to the cerebral network mechanism of depression.
Methods
Resting‐state fMRI scans were obtained from 70 medication‐free adults with MDD and 70 age‐ and sex‐matched healthy controls (HC). Functional connectivity maps of four distinct regions of the amygdala, including the amygdalostriatal transition area (AStr) and the basolateral (BLA), centromedial (CM) and superficial (SF) amygdala, were generated and compared between the two groups.
Results
Compared with HC, patients with MDD showed hypoconnectivity between the AStr/BLA and the orbitofrontal cortex (OFC), between the CM/SF and the brainstem/cerebellum, and within AStr/CM/SF‐thalamic/striatal networks. Hyperconnectivity was observed between the left AStr/BLA and the fusiform gyrus. There was no difference in the gray matter volume of the amygdala or any of its subregions between the two groups.
Conclusions
These findings suggest that amygdala subregional‐network dysfunction in MDD is independent of structural changes and, more important, that hypoconnectivity and hyperconnectivity in different subregional networks may reflect imbalanced network function, which may modulate different forms of emotional and cognitive dysfunction in MDD.
The precuneus is an association area in the posteromedial cortex (PMC) that is involved in high-order cognitive functions through integrating multi-modal information. Previous studies have shown that ...the precuneus is functionally heterogeneous and subdivided into several subfields organized by the anterior-posterior and ventral-dorsal axes. Further, the precuneus forms the structural core of brain connectivity as a rich-club hub and overlaps with the default mode network (DMN) as the functional core. This review summarizes recent research on the connectivity and cognitive functions of the precuneus. We then present our recent tractography-based studies of the precuneus and contextual these results here with respect to possible cognitive functions and resting-state networks.
Background
It is well established that even moderate levels of alcohol affect cognitive functions such as memory, self‐related information processing, and response inhibition. Nevertheless, the ...neural mechanisms underlying these alcohol‐induced changes are still unclear, especially on the network level. The default mode network (DMN) plays an important role in memory and self‐initiated mental activities; hence, studying functional interactions of the DMN may provide new insights into the neural mechanisms underlying alcohol‐related changes.
Methods
We investigated resting‐state functional connectivity (rsFC) of the DMN in a cohort of 37 heavy drinkers at a breath alcohol concentration of 0.8 g/kg. Alcohol and saline were infused in a single‐blind crossover design.
Results
Intranetwork connectivity analyses revealed that participants showed significantly decreased rsFC of the right hippocampus and right middle temporal gyrus during acute alcohol exposure. Moreover, follow‐up analyses revealed that these rsFC decreases were more pronounced in participants who reported stronger craving for alcohol. Exploratory internetwork connectivity analyses of the DMN with other resting‐state networks showed no significant alcohol‐induced changes, but suffered from low statistical power.
Conclusions
Our results indicate that acute alcohol exposure affects rsFC within the DMN. Functionally, this finding may be associated with impairments in memory encoding and self‐referential processes commonly observed during alcohol intoxication. Future resting‐state functional magnetic resonance imaging studies might therefore also investigate memory function and test whether DMN‐related connectivity changes are associated with alcohol‐induced impairments or craving.
Our study investigated the effects of alcohol (ALC) on functional brain connectivity in 37 heavy drinkers. Compared to saline (SAL), moderate alcohol exposure (0.8 g/kg) significantly decreased the functional connectivity of two brain areas within the default mode network (DMN). The DMN is a brain system implicated in memory processes and self‐initiated mentation. It is active when we are awake but resting. Our findings help explain dysfunctions in episodic memory encoding and self‐referential processing apparent after moderate alcohol consumption.
Abstract Introduction We performed a systematic review and meta-analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and ...mild cognitive impairment, using resting-state functional magnetic resonance imaging. Methods Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies. Results Thirty-four studies were included (1363 participants, average 40 per study). Consistent alterations in connectivity were found in the default mode, salience, and limbic networks in patients with AD dementia, mild cognitive impairment, or in both groups. We also identified a strong tendency in the literature toward specific examination of the default mode network. Discussion Convergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that highly connected hub regions in the brain might be an early target of AD.
Age-related changes in the brain connectivity of healthy older adults have been widely studied in recent years, with some differences in the obtained results. Most of these studies showed decreases ...in general functional connectivity, but they also found increases in some particular regions and areas. Frequently, these studies compared young individuals with older subjects, but few studies compared different age groups only in older populations. The purpose of this study is to analyze whole-brain functional connectivity in healthy older adult groups and its network characteristics through functional segregation. A total of 114 individuals, 48 to 89 years old, were scanned using resting-state functional magnetic resonance imaging in a resting state paradigm and were divided into six different age groups (< 60, 60-64, 65-69, 70-74, 75-79, ≥ 80 years old). A partial correlation analysis, a pooled correlation analysis and a study of 3-cycle regions with prominent connectivity were conducted. Our results showed progressive diminution in the functional connectivity among different age groups and this was particularly pronounced between 75 and 79 years old. The oldest group (≥ 80 years old) showed a slight increase in functional connectivity compared to the other groups. This occurred possibly because of compensatory mechanism in brain functioning. This study provides information on the brain functional characteristics of every age group, with more specific information on the functional progressive decline, and supplies methodological tools to study functional connectivity characteristics. Approval for the study was obtained from the ethics committee of the Comisión de Bioética de la Universidad de Barcelona (approval No. PSI2012-38257) on June 5, 2012, and from the ethics committee of the Barcelona's Hospital Clínic (approval No. 2009-5306 and 2011-6604) on October 22, 2009 and April 7, 2011 respectively.
Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post‐operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre‐ and ...intra‐operatively to delineate brain regions which are “eloquent” and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non‐invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre‐existing deficits. Connectome fingerprinting (CF) is a machine‐learning approach that learns associations between resting‐state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting‐state data. Here we utilized CF to train models on high‐quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting‐state fMRI (rs‐fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%–100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task‐related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out‐of‐sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments.
Practitioner Points
Precision motor network prediction using connectome fingerprinting.
Carefully trained models' performance limited by stability of task‐fMRI data.
Successful cross‐scanner predictions and motor network mapping in patients with tumor.
We used connectome fingerprinting to precisely map motor networks in healthy subjects after careful fine‐tuning of model parameters and used these models to make successful cross‐scanner predictions in clinical subjects and predicted motor region activity in presurgical patients with tumor to aid with neurosurgical planning.
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