Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest

Cortical functional connectivity, as indicated by the concurrent spontaneous activity of spatially segregated regions, is being studied increasingly because it may determine the reaction of the brain to external stimuli and task requirements and it is reportedly altered in many neurological and psychiatric disorders. In functional magnetic resonance imaging (fMRI), such functional connectivity is investigated commonly by correlating the time course of a chosen "seed voxel" with the remaining voxel time courses in a voxel-by-voxel manner. This approach is biased by the actual choice of the seed voxel, however, because it only shows functional connectivity for the chosen brain region while ignoring other potentially interesting patterns of coactivation. We used spatial independent component analysis (sICA) to assess cortical functional connectivity maps from resting state data. SICA does not depend on any chosen temporal profile of local brain activity. We hypothesized that sICA would be able to find functionally connected brain regions within sensory and motor regions in the absence of task-related brain activity. We also investigated functional connectivity patterns of several parietal regions including the superior parietal cortex and the posterior cingulate gyrus. The components of interest were selected in an automated fashion using predefined anatomical volumes of interest. SICA yielded connectivity maps of bilateral auditory, motor and visual cortices. Moreover, it showed that prefrontal and parietal areas are also functionally connected within and between hemispheres during the resting state. These connectivity maps showed an extremely high degree of consistency in spatial, temporal, and frequency parameters within and between subjects. These results are discussed in the context of the recent debate on the functional relevance of fluctuations of neural activity in the resting state.     

Effect of phase‐encoding direction on group analysis of resting‐state functional magnetic resonance imaging

Aim

Echo‐planar imaging is a common technique used in functional magnetic resonance imaging (fMRI); however, it suffers from image distortion and signal loss because of large susceptibility effects that are related to the phase‐encoding direction of the scan. Despite this relation, the majority of neuroimaging studies has not considered the influence of phase‐encoding direction. Here, we aimed to clarify how phase‐encoding direction can affect the outcome of an fMRI connectivity study of schizophrenia (SCZ).

Methods

Resting‐state fMRI using anterior to posterior (A–P) and posterior to anterior (P–A) directions was used to examine 25 patients with SCZ and 37 matched healthy controls (HC). We conducted a functional connectivity (FC) analysis using independent component analysis and performed three group comparisons: (i) A–P versus P–A (all participants); (ii) SCZ versus HC for the A–P and P–A datasets; and (iii) the interaction between phase‐encoding direction and participant group.

Results

The estimated FC differed between the two phase‐encoding directions in areas that were more extensive than those where signal loss has been reported. Although FC in the SCZ group was lower than that in the HC group for both directions, the A–P and P–A conditions did not exhibit the same specific pattern of differences. Further, we observed an interaction between participant group and the phase‐encoding direction in the left temporoparietal junction and left fusiform gyrus.

Conclusion

Phase‐encoding direction can influence the results of FC studies. Thus, appropriate selection and documentation of phase‐encoding direction will be important in future resting‐state fMRI studies.

     

Altered inter‐ and intrahemispheric functional connectivity dynamics in autistic children

Emerging evidence has associated autism spectrum disorder (ASD) with static functional connectivity abnormalities between multiple brain regions. However, the temporal dynamics of intra‐ and interhemispheric functional connectivity patterns remain unknown in ASD. Resting‐state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically matched typically developing control (TC) children (age range: 7–12 years) available from the Autism Brain Imaging Data Exchange database. Whole‐brain functional connectivity was decomposed into ipsilateral and contralateral functional connectivity, and sliding‐window analysis was utilized to capture the intra‐ and interhemispheric dynamic functional connectivity density (dFCD) patterns. The temporal variability of the functional connectivity dynamics was further quantified using the standard deviation (SD) of intra‐ and interhemispheric dFCD across time. Finally, a support vector regression model was constructed to assess the relationship between abnormal dFCD variance and autism symptom severity. Both intra‐ and interhemispheric comparisons showed increased dFCD variability in the anterior cingulate cortex/medial prefrontal cortex and decreased variability in the fusiform gyrus/inferior temporal gyrus in autistic children compared with TC children. Autistic children additionally showed lower intrahemispheric dFCD variability in sensorimotor regions including the precentral/postcentral gyrus. Moreover, aberrant temporal variability of the contralateral dFCD predicted the severity of social communication impairments in autistic children. These findings demonstrate altered temporal dynamics of the intra‐ and interhemispheric functional connectivity in brain regions incorporating social brain network of ASD, and highlight the potential role of abnormal interhemispheric communication dynamics in neural substrates underlying impaired social processing in ASD.     

Large‐scale functional brain network changes in taxi drivers: Evidence from resting‐state fMRI

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. Hum Brain Mapp 36:862–871, 2015. © 2014 Wiley Periodicals, Inc.     

Specific impairment of functional connectivity between language regions in former early preterms

Very preterm (PT) birth (≤32 weeks of gestation) carries a high risk for an adverse neurodevelopmental outcome. In recent years, the importance of neurocognitive deficits in the language domain has been increasingly recognized, which can be well‐characterized using neuropsychological testing and noninvasive imaging approaches. We compared former early PT born children and adolescents (PT, n = 29, 20M) and typically developing children (TD, n = 19, 7M), using conventional fMRI group analyses as well as functional connectivity analyses. We found only small regions with significantly different group activation (PT > TD) but significantly stronger connectivity between superior temporal lobe (STL) language regions in TD participants. There were also significant differences in local and global network efficiency (TD > PT). Surprisingly, there was a stronger connectivity of STL regions with non‐STL regions both intrahemispherically and interhemispherically in PT participants, suggesting the coexistence of reduced and increased connectivity in the language network of former PTs. Very similar results were obtained when using task‐based versus resting state functional connectivity approaches. Finally, lateralization of functional connectivity correlated with verbal comprehension abilities, suggesting that a more bilateral language comprehension representation is associated with better performance. Our results underline the importance of interhemispheric crosstalk for language comprehension. Hum Brain Mapp 35:3372–3384, 2014. © 2013 Wiley Periodicals, Inc .     

Baseline frontostriatal‐limbic connectivity predicts reward‐based memory formation

Reward mediates the acquisition and long‐term retention of procedural skills in humans. Yet, learning under rewarded conditions is highly variable across individuals and the mechanisms that determine interindividual variability in rewarded learning are not known. We postulated that baseline functional connectivity in a large‐scale frontostriatal‐limbic network could predict subsequent interindividual variability in rewarded learning. Resting‐state functional MRI was acquired in two groups of subjects (n = 30) who then trained on a visuomotor procedural learning task with or without reward feedback. We then tested whether baseline functional connectivity within the frontostriatal‐limbic network predicted memory strength measured immediately, 24 h and 1 month after training in both groups. We found that connectivity in the frontostriatal‐limbic network predicted interindividual variability in the rewarded but not in the unrewarded learning group. Prediction was strongest for long‐term memory. Similar links between connectivity and reward‐based memory were absent in two control networks, a fronto‐parieto‐temporal language network and the dorsal attention network. The results indicate that baseline functional connectivity within the frontostriatal‐limbic network successfully predicts long‐term retention of rewarded learning. Hum Brain Mapp 35:5921–5931, 2014. © 2014 Wiley Periodicals, Inc.     

The influence of mild carbon dioxide on brain functional homotopy using resting‐state fMRI

Homotopy reflects the intrinsic functional architecture of the brain through synchronized spontaneous activity between corresponding bilateral regions, measured as voxel mirrored homotopic connectivity (VMHC). Hypercapnia is known to have clear impact on brain hemodynamics through vasodilation, but have unclear effect on neuronal activity. This study investigates the effect of hypercapnia on brain homotopy, achieved by breathing 5% carbon dioxide (CO₂) gas mixture. A total of 14 healthy volunteers completed three resting state functional MRI (RS‐fMRI) scans, the first and third under normocapnia and the second under hypercapnia. VMHC measures were calculated as the correlation between the BOLD signal of each voxel and its counterpart in the opposite hemisphere. Group analysis was performed between the hypercapnic and normocapnic VMHC maps. VMHC showed a diffused decrease in response to hypercapnia. Significant regional decreases in VMHC were observed in all anatomical lobes, except for the occipital lobe, in the following functional hierarchical subdivisions: the primary sensory‐motor, unimodal, heteromodal, paralimbic, as well as in the following functional networks: ventral attention, somatomotor, default frontoparietal, and dorsal attention. Our observation that brain homotopy in RS‐fMRI is affected by arterial CO₂ levels suggests that caution should be used when comparing RS‐fMRI data between healthy controls and patients with pulmonary diseases and unusual respiratory patterns such as sleep apnea or chronic obstructive pulmonary disease. Hum Brain Mapp 36:3912–3921, 2015. © 2015 Wiley Periodicals, Inc.     

Fluctuations of the EEG‐fMRI correlation reflect intrinsic strength of functional connectivity in default mode network

Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting‐state functional connectivity is temporally variable in human brain. Combined full‐band electroencephalography‐fMRI (fbEEG‐fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood‐oxygen‐level‐dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG‐fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting‐state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short‐time‐window comparisons of infra‐slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG‐BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG‐fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG‐BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on‐average low correlations between infra‐slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.     

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging

Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience ( www.cam-can.com ). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc.     

Identifying dynamic functional connectivity biomarkers using GIG‐ICA: Application to schizophrenia,schizoaffective disorder,and psychotic bipolar disorder

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically‐related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group‐level and subject‐specific connectivity states from DFC. Using resting‐state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole‐brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis‐related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post‐central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD‐unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683–2708, 2017. © 2017 Wiley Periodicals, Inc.     

Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time

Resting‐state functional connectivity (FC) is highly variable across the duration of a scan. Groups of coevolving connections, or reproducible patterns of dynamic FC (dFC), have been revealed in fluctuating FC by applying unsupervised learning techniques. Based on results from k‐means clustering and sliding‐window correlations, it has recently been hypothesized that dFC may cycle through several discrete FC states. Alternatively, it has been proposed to represent dFC as a linear combination of multiple FC patterns using principal component analysis. As it is unclear whether sparse or nonsparse combinations of FC patterns are most appropriate, and as this affects their interpretation and use as markers of cognitive processing, the goal of our study was to evaluate the impact of sparsity by performing an empirical evaluation of simulated, task‐based, and resting‐state dFC. To this aim, we applied matrix factorizations subject to variable constraints in the temporal domain and studied both the reproducibility of ensuing representations of dFC and the expression of FC patterns over time. During subject‐driven tasks, dFC was well described by alternating FC states in accordance with the nature of the data. The estimated FC patterns showed a rich structure with combinations of known functional networks enabling accurate identification of three different tasks. During rest, dFC was better described by multiple FC patterns that overlap. The executive control networks, which are critical for working memory, appeared grouped alternately with externally or internally oriented networks. These results suggest that combinations of FC patterns can provide a meaningful way to disentangle resting‐state dFC. Hum Brain Mapp 35:5984–5995, 2014. © 2014 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.     

Source density-driven independent component analysis approach for fMRI data

Independent component analysis (ICA) has become a popular tool for functional magnetic resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax and FAST-ICA algorithms employ the underlying assumption that data can be decomposed into statistically independent sources and implicitly model the probability density functions of the underlying sources as highly kurtotic or symmetric. When source data violate these assumptions (e.g., are asymmetric), however, conventional ICA methods might not work well. As a result, modeling of the underlying sources becomes an important issue for ICA applications. We propose a source density-driven ICA (SD-ICA) method. The SD-ICA algorithm involves a two-step procedure. It uses a conventional ICA algorithm to obtain initial independent source estimates for the first-step and then, using a kernel estimator technique, the source density is calculated. A refitted nonlinear function is used for each source at the second step. We show that the proposed SD-ICA algorithm provides flexible source adaptivity and improves ICA performance. On SD-ICA application to fMRI signals, the physiologic meaningful components (e.g., activated regions) of fMRI signals are governed typically by a small percentage of the whole-brain map on a task-related activation. Extra prior information (using a skewed-weighted distribution transformation) is thus additionally applied to the algorithm for the regions of interest of data (e.g., visual activated regions) to emphasize the importance of the tail part of the distribution. Our experimental results show that the source density-driven ICA method can improve performance further by incorporating some a priori information into ICA analysis of fMRI signals.     

Unsupervised classification of major depression using functional connectivity MRI

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self‐reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting‐state functional magnetic resonance imaging scans in the absence of clinical information. Twenty‐four medication‐naive patients with major depression and 29 demographically similar healthy individuals underwent resting‐state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting‐state functional connectivity patterns and showed that a maximum margin clustering‐based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group‐level clustering consistency of 92.5% and an individual‐level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering‐based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Hum Brain Mapp 35:1630–1641, 2014. © 2013 Wiley Periodicals, Inc.     

Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi‐view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l_2,1 ‐norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group‐sparse network at local level. We then linearly fuse the selected features from each individual network via a multi‐kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.