Search

Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networks were identified through independent component analysis (ICA) of time-varying dFCD maps. Furthermore, a systematical comparison with commonly used brain atlases, including the Anatomical Automatic Labeling template, static ICA-driven parcellation and random parcellation, demonstrated that the ROI-definition strategy based on the proposed dFC-driven parcellation could better capture the interindividual variability in dFC and predict observed individual cognitive performance (e.g., fluid intelligence, cognitive flexibility, and sustained attention) based on chronnectome. Together, our findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC-driven and voxel-wise functional homogeneous parcellation for network dynamics analyses in neuroscience., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The neurophysiological basis of spontaneous low-frequency brain activity has become a major theme in the study of neural function in both humans and animal models. In such studies, the anesthesia model was generally adopted. However, the effects of anesthesia on spontaneous activity remain unclear. In this work, we explored the characteristics of cerebral spontaneous low-frequency activities at different depths of anesthesia in mice. Using Fourier transformation and the multitaper analysis methods, spontaneous low-frequency oscillations (LFOs) in the intrinsic signals of different cerebral regions (artery, vein, and cortex) were extracted and analyzed. Under different concentrations of anesthetic, the frequency of spontaneous LFO signals remained stable, while LFO amplitudes increased with the depth of anesthesia. The results imply that the anesthetic impacts the amplitude of spontaneous LFOs but does not alter the oscillation frequency.

The current study aimed to investigate the altered cerebellar-cerebral functional connectivity (FC) induced by radiotherapy to nasopharyngeal carcinoma (NPC) patients. Twenty-four NPC patients without treatment, and 35 NPC patients receiving radiotherapy underwent functional MRI scanning. Montreal cognitive assessment (MoCA) was performed to evaluate the cognitive status of all participants. FC between 10 predefined cerebellar seeds, which were demonstrated to be involved in different brain functional networks, and all brain voxels was obtained for each participant. Using a second-level two-sample t-test, three significantly different FCs between the two patient groups were found, including the connections between the left lobule VIII and the right medial frontal gyrus, the left lobule VIII and the right crus I, and the right lobule VIIb and the right fusiform gyrus. The altered cerebellar-cerebral FCs were also significantly correlated to the MoCA score, as well as the attention score, one of the seven subscores in MoCA. We suggested that the altered cerebellar-cerebral FCs may underlie the radiation-induced cognitive deficits in NPC patients, especially in the domain of attention. Furthermore, considering the functional networks in which the altered connections involved, the anticorrelation between the default network and dorsal attention network may be impaired, and the mediating function of the frontoparietal network to dorsal attention network may be disrupted. The significantly altered cerebellar-cerebral FC may serve as the potential biomarker in revealing the radiation-induced functional abnormalities and may help in the early intervention to the cognitive impairment.

Antisocial personality disorder (ASPD), one of whose characteristics is high impulsivity, is of great interest in the field of brain structure and function. However, little is known about possible impairments in the cortical anatomy in ASPD, in terms of cortical thickness (CTh) and surface area (SA), as well as their possible relationship with impulsivity. In this neuroimaging study, we first investigated the changes of CTh and SA in ASPD patients, in comparison to those of healthy controls, and then performed correlation analyses between these measures and the ability of impulse control. We found that ASPD patients showed thinner cortex while larger SA in several specific brain regions, i.e., bilateral superior frontal gyrus (SFG), orbitofrontal and triangularis, insula cortex, precuneus, middle frontal gyrus (MFG), middle temporal gyrus (MTG), and left bank of superior temporal sulcus (STS). In addition, we also found that the ability of impulse control was positively correlated with CTh in the SFG, MFG, orbitofrontal cortex (OFC), pars triangularis, superior temporal gyrus (STG), and insula cortex. To our knowledge, this study is the first to reveal simultaneous changes in CTh and SA in ASPD, as well as their relationship with impulsivity. These cortical structural changes may introduce uncontrolled and callous behavioral characteristic in ASPD patients, and these potential biomarkers may be very helpful in understanding the pathomechanism of ASPD., (Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.)

Objective: The pathogenesis of benign adult familial myoclonic epilepsy (BAFME) remains unknown, although cerebellar pathologic changes and brain hyperexcitability have been reported. We used resting-state functional magnetic resonance imaging (fMRI) to examine the functional connectivity between the cerebellum and cerebrum in a Chinese family with BAFME for the first time., Methods: Eleven adults with BAFME and 15 matched healthy controls underwent resting-state blood oxygen level-dependent (BOLD) fMRI scanning. The cerebellar seeds, including the bilateral crus I, lobule VIII, lobule VIIb, and lobule IV&V, were defined a priori. Next, regional time courses were obtained for each individual by averaging the BOLD time series over all voxels in each seed region. Then, seed-based functional connectivity z-maps were produced by computing Pearson's correlation coefficients (converted to z-scores by Fisher transformation) between each seed signal and the time series from all other voxels within the entire brain. Finally, a second-level random-effect two-sample t-test was performed on the individual z-maps in a voxel-wise manner., Results: Reduced functional connectivity of the right cerebellar crus I with the left middle frontal gyrus and right cerebellar lobule IX was observed in the default network of BAFME. Enhanced functional connectivity of the left cerebellar lobule VIII with the bilateral middle temporal gyri, right putamen, and left cerebellar crus I was found in the dorsal attention network of BAFME. Enhanced functional connectivity between the left cerebellar lobule VIIb and right frontal pole was found in the control network of BAFME., Significance: Altered cerebellar-cerebral functional connectivity may contribute to the understanding of the nosogenesis of BAFME and explain the cognitive dysfunction in this Chinese family with BAFME., (Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.)

Driving a car in the environment is a complex behavior that involves cognitive processing of visual information to generate the proper motor outputs and action controls. Previous neuroimaging studies have used virtual simulation to identify the brain areas that are associated with various driving-related tasks. Few studies, however, have focused on the specific patterns of functional organization in the driver's brain. The aim of this study was to assess differences in the resting-state networks (RSNs) of the brains of drivers and nondrivers. Forty healthy subjects (20 licensed taxi drivers, 20 nondrivers) underwent an 8-min resting-state functional MRI acquisition. Using independent component analysis, three sensory (primary and extrastriate visual, sensorimotor) RSNs and four cognitive (anterior and posterior default mode, left and right frontoparietal) RSNs were retrieved from the data. We then examined the group differences in the intrinsic brain activity of each RSN and in the functional network connectivity (FNC) between the RSNs. We found that the drivers had reduced intrinsic brain activity in the visual RSNs and reduced FNC between the sensory RSNs compared with the nondrivers. The major finding of this study, however, was that the FNC between the cognitive and sensory RSNs became more positively or less negatively correlated in the drivers relative to that in the nondrivers. Notably, the strength of the FNC between the left frontoparietal and primary visual RSNs was positively correlated with the number of taxi-driving years. Our findings may provide new insight into how the brain supports driving behavior., (© 2014 Wiley Periodicals, Inc.)

Objectives: The thalamus and cerebral cortex are connected via topographically organized, reciprocal connections, which hold a key function in segregating internally and externally directed awareness information. Previous task-related studies have revealed altered activities of the thalamus after total sleep deprivation (TSD). However, it is still unclear how TSD impacts on the communication between the thalamus and cerebral cortex. In this study, we examined changes of thalamocortical functional connectivity after 36 hours of total sleep deprivation by using resting state function MRI (fMRI)., Materials and Methods: Fourteen healthy volunteers were recruited and performed fMRI scans before and after 36 hours of TSD. Seed-based functional connectivity analysis was employed and differences of thalamocortical functional connectivity were tested between the rested wakefulness (RW) and TSD conditions., Results: We found that the right thalamus showed decreased functional connectivity with the right parahippocampal gyrus, right middle temporal gyrus and right superior frontal gyrus in the resting brain after TSD when compared with that after normal sleep. As to the left thalamus, decreased connectivity was found with the right medial frontal gyrus, bilateral middle temporal gyri and left superior frontal gyrus., Conclusion: These findings suggest disruptive changes of the thalamocortical functional connectivity after TSD, which may lead to the decline of the arousal level and information integration, and subsequently, influence the human cognitive functions.

In recent years, the cerebellum has been demonstrated to be involved in cognitive control and emotional processing and to play an important role in the pathology of major depressive disorder (MDD). The current study aims to explore the potential utility of selecting the altered cerebellar-cerebral functional connectivity as a classification feature to discriminate depressed patients from healthy controls. Twenty-four medication-free patients with major depression and 29 matched, healthy controls underwent resting-state functional magnetic resonance imaging. A promising classification accuracy of 90.6% was achieved using resting-state functional connectivity between predefined cerebellar seed regions and the voxels within the cerebrum as features. Moreover, the most discriminating functional connections were mainly located between the cerebellum and the anterior cingulate cortex, the ventromedial prefrontal cortex, the ventrolateral prefrontal cortex, the temporal lobe and the fusiform gyrus, which may contribute to the emotional and cognitive impairments observed in major depression. The current findings imply that the cerebellum might be considered as a node in the distributed disease-related brain network in major depression., (Copyright © 2012 Elsevier B.V. All rights reserved.)

An automated method is presented for artery-vein separation in cerebral cortical images recorded with optical imaging of the intrinsic signal. The vessel-type separation method is based on the fact that the spectral distribution of intrinsic physiological oscillations varies from arterial regions to venous regions. In arterial regions, the spectral power is higher in the heartbeat frequency (HF), whereas in venous regions, the spectral power is higher in the respiration frequency (RF). The separation method was begun by extracting the vascular network and its centerline. Then the spectra of the optical intrinsic signals were estimated by the multitaper method. A standard F-test was performed on each discrete frequency point to test the statistical significance at the given level. Four periodic physiological oscillations were examined: HF, RF, and two other eigenfrequencies termed F1 and F2. The separation of arteries and veins was implemented with the fuzzy c-means clustering method and the region-growing approach by utilizing the spectral amplitudes and power-ratio values of the four eigenfrequencies on the vasculature. Subsequently, independent spectral distributions in the arteries, veins, and capillary bed were estimated for comparison, which showed that the spectral distributions of the intrinsic signals were very distinct between the arterial and venous regions.

Voxel-based morphometry (VBM) is an objective whole-brain technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. In the current study, we used VBM to examine possible cerebral gray matter abnormalities in patients with posttraumatic stress disorder (PTSD) due to fire. The subjects included 12 victims of a fire disaster with PTSD and 12 matched victims of the same fire without PTSD. Magnetic resonance images were obtained on a 1.5-Tesla General Electric scanner at Central South University, and an entire brain volume of 248 contiguous slices was obtained for each subject. Then, gray matter density in patients with PTSD and control groups was compared by using a VBM approach in SPM2. Group analysis was thresholded at P<0.001, uncorrected, at the voxel level. The following three regions of reduced gray matter volume were found in patients with PTSD compared with controls: left hippocampus, left anterior cingulate cortex (ACC), and bilateral insular cortex. It was concluded that there are structural abnormalities of the hippocampus, the ACC and the insular cortex in patients with PTSD due to fire.

A widely used tool for functional magnetic resonance imaging (fMRI) data analysis, statistical parametric mapping (SPM), is based on the general linear model (GLM). SPM therefore requires a priori knowledge or specific assumptions about the time courses contributing to signal changes. In contradistinction, independent component analysis (ICA) is a data-driven method based on the assumption that the causes of responses are statistically independent. Here we describe a unified method, which combines ICA, temporal ICA (tICA), and SPM for analyzing fMRI data. tICA was applied to fMRI datasets to disclose independent components, whose number was determined by the Bayesian information criterion (BIC). The resulting components were used to construct the design matrix of a GLM. Parameters were estimated and regionally-specific statistical inferences were made about activations in the usual way. The sensitivity and specificity were evaluated using Monte Carlo simulations. The receiver operating characteristic (ROC) curves indicated that the unified SPM-ICA method had a better performance. Moreover, SPM-ICA was applied to fMRI datasets from twelve normal subjects performing left and right hand movements. The areas identified corresponded to motor (premotor, sensorimotor areas and SMA) areas and were consistently task related. Part of the frontal lobe, parietal cortex, and cingulate gyrus also showed transiently task-related responses. The unified method requires less supervision than the conventional SPM and enables classical inference about the expression of independent components. Our results also suggest that the method has a higher sensitivity than SPM analyses.

The human cerebral cortex is vastly expanded relative to nonhuman primates and rodents, leading to a functional orderly topography of brain networks. Here, we show that functional topography may be associated with gene expression heterogeneity. The neocortex exhibits greater heterogeneity in gene expression, with a lower expression of housekeeping genes, a longer mean path length, fewer clusters, and a lower degree of ordering in networks than archicortical and subcortical areas in human, rhesus macaque, and mouse brains. In particular, the cerebellar cortex displays greater heterogeneity in gene expression than cerebellar deep nuclei in the human brain, but not in the mouse brain, corresponding to the emergence of novel functions in the human cerebellar cortex. Moreover, the cortical areas with greater heterogeneity, primarily located in the multimodal association cortex, tend to express genes with higher evolutionary rates and exhibit a higher degree of functional connectivity measured by resting-state fMRI, implying that such a spatial distribution of gene expression may be shaped by evolution and is favourable for the specialization of higher cognitive functions. Together, the cross-species imaging and genetic findings may provide convergent evidence to support the association between the orderly topography of brain function networks and gene expression. Comparative analysis of human, macaque and mouse function and genetic heterogeneity in the brain reveals links between gene expression and orderly topography of functional networks. [ABSTRACT FROM AUTHOR]

Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we proposed a local region structured noise reduction method for cortical optical imaging (OI). In our method, block-designed task paradigm was employed. Canonical correlation analysis (CCA) technique was used to extract the underlying structured sources voxel by voxel. The response signals were detected among structured sources by surrogate test based on the reduced autoregression model (ST-RARM) technique. The power of structured noise was eliminated from original time series and then the data were reconstructed. Monte-Carlo simulation was applied to demonstrate the validity of our method. The results showed that our method was more efficient in activated voxel detection compared to the generally used methods PCA, DCT. Further, by using our method the phase knowledge of response signals was well preserved in the reconstructed data and hence a more accurate estimate was obtained. The final activity mapping was generated by utilizing the knowledge of both response amplitude and phase. The vein artifacts were efficiently reduced. Six sets of true OI data collected from the hind-paw (HP) area of rat''s cortex were processed and improved activity mappings were obtained. [Copyright &y& Elsevier]

Currently, 422 million adults suffer from diabetes worldwide, leading to tremendous disabilities and a great burden to families and society. Functional and structural MRIs have demonstrated that patients with type 2 diabetes mellitus (T2DM) exhibit abnormalities in brain regions in the cerebral cortex. However, the changes of cerebellar anatomical connections in diabetic patients remains unclear. In the current study, diffusion tensor imaging deterministic tractography and statistical analysis were employed to investigate abnormal cerebellar anatomical connections in diabetic patients. This is the first study to investigate the altered cerebellar anatomical connectivity in T2DM patients. Decreased anatomical connections were found in the cerebellar and cerebro-cerebellar circuits of T2DM patients, providing valuable new insights into the potential neuro-pathophysiology of diabetes-related motor and cognitive deficits. [ABSTRACT FROM AUTHOR]