High‐order resting‐state functional connectivity network for MCI classification

Brain functional connectivity (FC) network, estimated with resting‐state functional magnetic resonance imaging (RS‐fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low‐order in the sense that only the correlations among brain regions (in terms of RS‐fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high‐order FC correlations that characterize how the low‐order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS‐fMRI time series to generate multiple short overlapping segments. For each segment, a low‐order FC network is constructed, measuring the short‐term correlation between brain regions. These low‐order networks (obtained from all segments) describe the dynamics of short‐term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high‐order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low‐order and high‐order FC networks. Experimental results verify the effectiveness of the high‐order FC network on disease diagnosis. Hum Brain Mapp 37:3282–3296, 2016. © 2016 Wiley Periodicals, Inc.     

Breakdown of the affective‐cognitive network in functional dystonia

Previous studies suggested that brain regions subtending affective‐cognitive processes can be implicated in the pathophysiology of functional dystonia (FD). In this study, the role of the affective‐cognitive network was explored in two phenotypes of FD: fixed (FixFD) and mobile dystonia (MobFD). We hypothesized that each of these phenotypes would show peculiar functional connectivity (FC) alterations in line with their divergent disease clinical expressions. Resting state fMRI (RS‐fMRI) was obtained in 40 FD patients (12 FixFD; 28 MobFD) and 43 controls (14 young FixFD‐age‐matched [yHC]; 29 old MobFD‐age‐matched [oHC]). FC of brain regions of interest, known to be involved in affective‐cognitive processes, and independent component analysis of RS‐fMRI data to explore brain networks were employed. Compared to HC, all FD patients showed reduced FC between the majority of affective‐cognitive seeds of interest and the fronto‐subcortical and limbic circuits; enhanced FC between the right affective‐cognitive part of the cerebellum and the bilateral associative parietal cortex; enhanced FC of the bilateral amygdala with the subcortical and posterior cortical brain regions; and altered FC between the left medial dorsal nucleus and the sensorimotor and associative brain regions (enhanced in MobFD and reduced in FixFD). Compared with yHC and MobFD patients, FixFD patients had an extensive pattern of reduced FC within the cerebellar network, and between the majority of affective‐cognitive seeds of interest and the sensorimotor and high‐order function (“cognitive”) areas with a unique involvement of dorsal anterior cingulate cortex connectivity. Brain FC within the affective‐cognitive network is altered in FD and presented specific features associated with each FD phenotype, suggesting an interaction between brain connectivity and clinical expression of the disease.     

Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task‐based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within‐network or between‐network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large‐scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample‐size resting‐state fMRI consisting of 100 healthy subjects and 100 individuals with first‐episode drug‐naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high‐order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network‐averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self‐recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high‐order brain functional networks.     

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Brain functional connectivity (FC) extracted from resting‐state fMRI (RS‐fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co‐variations of the blood oxygenation level‐dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS‐fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor‐based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS‐fMRI data. Moreover, a sliding window approach is further used to partition the voxel‐wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS‐fMRI data alone. Hum Brain Mapp 38:5019–5034, 2017. © 2017 Wiley Periodicals, Inc.     

Wavelet‐based regularity analysis reveals recurrent spatiotemporal behavior in resting‐state fMRI

One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large‐scale networks. In resting state fMRI (rs‐fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs‐fMRI signal. Here, we develop a wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs‐fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of “lagged” subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an “objective” estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs‐fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet‐based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs‐fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs‐fMRI as well as other biological signals. Hum Brain Mapp 36:3603–3620, 2015. © 2015 Wiley Periodicals, Inc.     

Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder

Resting‐state fMRI (RS‐fMRI) has become a useful tool to investigate the connectivity structure of mental health disorders. In the case of major depressive disorder (MDD), recent studies regarding the RS‐fMRI have found abnormal connectivity in several regions of the brain, particularly in the default mode network (DMN). Thus, the relevance of the DMN to self‐referential thoughts and ruminations has made the use of the resting‐state approach particularly important for MDD. The majority of such research has relied on the grand averaged functional connectivity measures based on the temporal correlations between the BOLD time series of various brain regions. We, in our study, investigated the variations in the functional connectivity over time at global and local level using RS‐fMRI BOLD time series of 27 MDD patients and 27 healthy control subjects. We found that global synchronization and temporal stability were significantly increased in the MDD patients. Furthermore, the participants with MDD showed significantly increased overall average (static) functional connectivity (sFC) but decreased variability of functional connectivity (vFC) within specific networks. Static FC increased to predominance among the regions pertaining to the default mode network (DMN), while the decreased variability of FC was observed in the connections between the DMN and the frontoparietal network. Hum Brain Mapp 37:2918–2930, 2016. © 2016 Wiley Periodicals, Inc.     

Musical training induces functional and structural auditory‐motor network plasticity in young adults

Playing music requires a strong coupling of perception and action mediated by multimodal integration of brain regions, which can be described as network connections measured by anatomical and functional correlations between regions. However, the structural and functional connectivities within and between the auditory and sensorimotor networks after long‐term musical training remain largely uninvestigated. Here, we compared the structural connectivity (SC) and resting‐state functional connectivity (rs‐FC) within and between the two networks in 29 novice healthy young adults before and after musical training (piano) with those of another 27 novice participants who were evaluated longitudinally but with no intervention. In addition, a correlation analysis was performed between the changes in FC or SC with practice time in the training group. As expected, participants in the training group showed increased FC within the sensorimotor network and increased FC and SC of the auditory‐motor network after musical training. Interestingly, we further found that the changes in FC within the sensorimotor network and SC of the auditory‐motor network were positively correlated with practice time. Our results indicate that musical training could induce enhanced local interaction and global integration between musical performance‐related regions, which provides insights into the mechanism of brain plasticity in young adults.     

Sex‐related differences in resting‐state brain activity and connectivity in the orbital frontal cortex and insula in patients with functional constipation

Functional magnetic resonance imaging (fMRI) has been used to investigate sex‐related differences in brain abnormalities in patients with irritable bowel syndrome (IBS). Like IBS, women with functional constipation (FC) are 2.1 times as many as men. No study has been performed yet to examine sex‐related differences in brain activity and connectivity in patients with FC. Here, we employed resting‐state fMRI with amplitude of low‐frequency fluctuation (ALFF) to investigate brain functional differences in 51 patients with FC (34 females) and 52 healthy controls (34 females). Results showed abdominal pain and abdominal distension correlated with trait (TAI) and state (SAI) anxiety ratings in the female FC group, and abdominal distension correlated with sensation of incomplete evacuation in the male FC group. Two‐way ANOVA revealed sex effects on ALFF in precentral gyrus, thalamus, insula (INS), and orbital frontal cortex (OFC, P_FWE < 0.05). Post hoc test showed that the female FC group had lower ALFF than males in these brain regions (P < 0.01), and ALFF in INS and OFC was correlated with abdominal pain and difficulty of defecation, respectively. Seed voxel correlation analysis showed that the female FC group had weaker connectivity than males between INS and lateral OFC (lOFC). INS‐lOFC connectivity was negatively correlated with the anxiety score in the female FC group and was negatively correlated with abdominal distension in the male FC group. These findings provide the first insight into sex‐related differences in patients with FC and highlight that INS and OFC play an important role in modulating the intrinsic functional connectivity of the resting brain network showing that this role is influenced by sex.     

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white‐matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white‐matter structural architecture, it cannot detect neural activity or white‐matter functions. Recent studies demonstrated the functional organization of white‐matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white‐matter dysfunctions in BECT. Resting‐state fMRI data were collected from 24 new‐onset drug‐naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white‐matter functional networks were obtained using a clustering analysis on voxel‐by‐voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub‐bands (Slow‐5:0.01–0.027, Slow‐4:0.027–0.073, Slow‐3:0.073–0.198, and Slow‐2:0.198–0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white‐matter networks based on their correlations with multiple gray‐matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency‐specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow‐3 and Slow‐4. This study provided further support on the existence of white‐matter functional networks which exhibited frequency‐specific properties, and extended abnormalities of rolandic area from the perspective of white‐matter dysfunction in BECT.     

The association between resting‐state functional magnetic resonance imaging and aortic pulse‐wave velocity in healthy adults

Resting‐state functional magnetic resonance imaging (rs‐fMRI) is frequently used to study brain function; but, it is unclear whether BOLD‐signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascular‐health factors are reflected in rs‐fMRI metrics in the healthy population. As arterial stiffening is a known age‐related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse‐wave velocity [PWV]) and rs‐fMRI metrics. We used cardiac MRI to measure aortic PWV (an established indicator of whole‐body vascular stiffness), as well as dual‐echo pseudo‐continuous arterial‐spin labeling to measure BOLD and CBF dynamics simultaneously in a group of generally healthy adults. We found that: (1) higher aortic PWV is associated with lower variance in the resting‐state BOLD signal; (2) higher PWV is also associated with lower BOLD‐based resting‐state functional connectivity; (3) regions showing lower connectivity do not fully overlap with those showing lower BOLD variance with higher PWV; (4) CBF signal variance is a significant mediator of the above findings, only when averaged across regions‐of‐interest. Furthermore, we found no significant association between BOLD signal variance and systolic blood pressure, which is also a known predictor of vascular stiffness. Age‐related vascular stiffness, as measured by PWV, provides a unique scenario to demonstrate the extent of vascular bias in rs‐fMRI signal fluctuations and functional connectivity. These findings suggest that a substantial portion of age‐related rs‐fMRI differences may be driven by vascular effects rather than directly by brain function.     

Spatio‐temporal dynamics of resting‐state brain networks improve single‐subject prediction of schizophrenia diagnosis

Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding‐window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting‐state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting‐state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting‐state dynamics are not trivially attributable to sampling variability and head motion.     

Disrupted functional connectivity of cerebellar default network areas in attention‐deficit/hyperactivity disorder

Attention‐deficit/hyperactivity disorder (ADHD) is increasingly understood as a disorder of spontaneous brain‐network interactions. The default mode network (DMN), implicated in ADHD‐linked behaviors including mind‐wandering and attentional fluctuations, has been shown to exhibit abnormal spontaneous functional connectivity (FC) within‐network and with other networks (salience, dorsal attention and frontoparietal) in ADHD. Although the cerebellum has been implicated in the pathophysiology of ADHD, it remains unknown whether cerebellar areas of the DMN (CerDMN) exhibit altered FC with cortical networks in ADHD. Here, 23 adults with ADHD and 23 age‐, IQ‐, and sex‐matched controls underwent resting state fMRI. The mean time series of CerDMN areas was extracted, and FC with the whole brain was calculated. Whole‐brain between‐group differences in FC were assessed. Additionally, relationships between inattention and individual differences in FC were assessed for between‐group interactions. In ADHD, CerDMN areas showed positive FC (in contrast to average FC in the negative direction in controls) with widespread regions of salience, dorsal attention and sensorimotor networks. ADHD individuals also exhibited higher FC (more positive correlation) of CerDMN areas with frontoparietal and visual network regions. Within the control group, but not in ADHD, participants with higher inattention had higher FC between CerDMN and regions in the visual and dorsal attention networks. This work provides novel evidence of impaired CerDMN coupling with cortical networks in ADHD and highlights a role of cerebro‐cerebellar interactions in cognitive function. These data provide support for the potential targeting of CerDMN areas for therapeutic interventions in ADHD. Hum Brain Mapp 36:3373–3386, 2015. © 2015 Wiley Periodicals, Inc.     

Dual‐echo ASL contributes to decrypting the link between functional connectivity and cerebral blow flow

Arterial spin labeling (ASL) MRI with a dual‐echo readout module (DE‐ASL) enables noninvasive simultaneous acquisition of cerebral blood flow (CBF)‐weighted images and blood oxygenation level dependent (BOLD) contrast. Up to date, resting‐state functional connectivity (FC) studies based on CBF fluctuations have been very limited, while the BOLD is still the method most frequently used. The purposes of this technical report were (i) to assess the potentiality of the DE‐ASL sequence for the quantification of resting‐state FC and brain organization, with respect to the conventional BOLD (cvBOLD) and (ii) to investigate the relationship between a series of complex network measures and the CBF information. Thirteen volunteers were scanned on a 3 T scanner acquiring a pseudocontinuous multislice DE‐ASL sequence, from which the concomitant BOLD (ccBOLD) simultaneously to the ASL can be extracted. In the proposed comparison, the brain FC and graph‐theoretical analysis were used for quantifying the connectivity strength between pairs of regions and for assessing the network model properties in all the sequences. The main finding was that the ccBOLD part of the DE‐ASL sequence provided highly comparable connectivity results compared to cvBOLD. As expected, because of its different nature, ASL sequence showed different patterns of brain connectivity and graph indices compared to BOLD sequences. To conclude, the resting‐state FC can be reliably detected using DE‐ASL, simultaneously to CBF quantifications, whereas a single fMRI experiment precludes the quantitative measurement of BOLD signal changes. Hum Brain Mapp 38:5831–5844, 2017. © 2017 Wiley Periodicals, Inc.     

Hubs disruption in mesial temporal lobe epilepsy. A resting‐state fMRI study on a language‐and‐memory network

Mesial temporal lobe epilepsy (mTLE) affects the brain networks at several levels and patients suffering from mTLE experience cognitive impairment for language and memory. Considering the importance of language and memory reorganization in this condition, the present study explores changes of the embedded language‐and‐memory network (LMN) in terms of functional connectivity (FC) at rest, as measured with functional MRI. We also evaluate the cognitive efficiency of the reorganization, that is, whether or not the reorganizations support or allow the maintenance of optimal cognitive functioning despite the seizure‐related damage. Data from 37 patients presenting unifocal mTLE were analyzed and compared to 48 healthy volunteers in terms of LMN‐FC using two methods: pairwise correlations (region of interest [ROI]‐to‐ROI) and graph theory. The cognitive efficiency of the LMN‐FC reorganization was measured using correlations between FC parameters and language and memory scores. Our findings revealed a large perturbation of the LMN hubs in patients. We observed a hyperconnectivity of limbic areas near the dysfunctional hippocampus and mainly a hypoconnectivity for several cortical regions remote from the dysfunctional hippocampus. The loss of FC was more important in left mTLE (L‐mTLE) than in right (R‐mTLE) patients. The LMN‐FC reorganization may not be always compensatory and not always useful for patients as it may be associated with lower cognitive performance. We discuss the different connectivity patterns obtained and conclude that interpretation of FC changes in relation to neuropsychological scores is important to determine cognitive efficiency, suggesting the concept of “connectome” would gain to be associated with a “cognitome” concept.     

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.     

Sensorimotor network alterations in children and youth with prenatal alcohol exposure

Children with prenatal alcohol exposure (PAE) often have impaired sensorimotor function. While altered brain structure has been noted in sensorimotor areas, the functional brain alterations remain unclear. This study aims to investigate sensorimotor brain networks in children and youth with PAE using resting‐state functional magnetic resonance imaging (rs‐fMRI). A parcellation‐based network analysis was performed to identify brain networks related to hand/lower limb and face/upper limb function in 59 children and youth with PAE and 50 typically developing controls. Participants with PAE and controls had similar organization of the hand and face areas within the primary sensorimotor cortex, but participants with PAE had altered functional connectivity (FC) between the sensorimotor regions and the rest of the brain. The sensorimotor regions in the PAE group showed less connectivity to certain hubs of the default mode network and more connectivity to areas of the salience network. Overall, our results show that despite similar patterns of organization in the sensorimotor network, subjects with PAE have increased FC between this network and other brain areas, perhaps suggesting overcompensation. These alterations in the sensorimotor network lay the foundation for future studies to evaluate interventions and treatments to improve motor function in children with PAE.     

Functional MRI registration with tissue‐specific patch‐based functional correlation tensors

Population studies of brain function with resting‐state functional magnetic resonance imaging (rs‐fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high‐resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs‐fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole‐brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white‐matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level‐dependent (BOLD) signals using tissue‐specific patch‐based functional correlation tensors (ts‐PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi‐channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods.     

Amisulpride and l‐DOPA modulate subcortical brain nuclei connectivity in resting‐state pharmacologic magnetic resonance imaging

The precise understanding of the dopaminergic (DA) system and its pharmacological modifications is crucial for diagnosis and treatment of neuropsychiatric disorders, as well as for understanding basic processes, such as motivation and reward. We probed the functional connectivity (FC) of subcortical nuclei related to the DA system according to seed regions defined according to an atlas of subcortical nuclei. We conducted a large pharmaco‐fMRI study using a double‐blind, placebo‐controlled design, where we examined the effect of l ‐DOPA, a dopamine precursor, and amisulpride, a D2/D3‐receptor antagonist on resting‐state FC in 45 healthy young adults using a cross‐over design. We examined the FC of subcortical nuclei with connection to the reward system and their reaction to opposing pharmacological probing. Amisulpride increased FC from the putamen to the precuneus and from ventral striatum to precentral gyrus. l ‐DOPA increased FC from the ventral tegmental area (VTA) to the insula/operculum and between ventral striatum and ventrolateral prefrontal cortex and it disrupted ventral striatal and dorsal caudate FC with the medial prefrontal cortex. In an exploratory analysis, we demonstrated that higher self‐rated impulsivity goes together with a significant increase in VTA‐mid‐cingulate gyrus FC during l ‐DOPA‐challenge. Therefore, our DA challenge modulated distinct large‐scale subcortical connectivity networks. A dopamine‐boost can increase midbrain DA nuclei connectivity to the cortex. The involvement of the VTA‐cingulum connectivity in dependence of impulsivity has implications for diagnosis and therapy in disorders like ADHD.     

Resting‐state fMRI detects the effects of learning in short term: A visual search training study

Can resting‐state functional connectivity (rs‐FC) detect the impact of learning on the brain in the short term? To test this possibility, we have combined task‐FC and rs‐FC tested before and after a 30‐min visual search training. Forty‐two healthy adults (20 men) divided into no‐contact control and trained groups completed the study. We studied the connectivity between four different regions of the brain involved in visual search: the primary visual area, the right posterior parietal cortex (rPPC), the right dorsolateral prefrontal cortex (rDLPFC), and the dorsal anterior cingulate cortex (dACC). Task‐FC showed increased connectivity between the rPPC and rDLPFC and between the dACC and rDLPFC from pretraining to posttraining for both the control group and the trained group, suggesting that connectivity between these areas increased with task repetition. In rs‐FC, we found enhanced connectivity between these regions in the trained group after training, especially in those with better learning. Whole brain independent component analyses did not reveal any change in main networks after training. These results imply that rs‐FC may not only predict individual differences in task performance, but rs‐FC might also serve to monitor the impact of learning on the brain after short periods of cognitive training, localizing them in brain areas specifically involved in training.     

Localized reductions in resting‐state functional connectivity in children with prenatal alcohol exposure

Fetal alcohol spectrum disorders (FASD) are characterized by impairment in cognitive function that may or may not be accompanied by craniofacial anomalies, microcephaly, and/or growth retardation. Resting‐state functional MRI (rs‐fMRI), which examines the low‐frequency component of the blood oxygen level dependent (BOLD) signal in the absence of an explicit task, provides an efficient and powerful mechanism for studying functional brain networks even in low‐functioning and young subjects. Studies using independent component analysis (ICA) have identified a set of resting‐state networks (RSNs) that have been linked to distinct domains of cognitive and perceptual function, which are believed to reflect the intrinsic functional architecture of the brain. This study is the first to examine resting‐state functional connectivity within these RSNs in FASD. Rs‐fMRI scans were performed on 38 children with FASD (19 with either full fetal alcohol syndrome (FAS) or partial FAS (PFAS), 19 nonsyndromal heavily exposed (HE)), and 19 controls, mean age 11.3 ± 0.9 years, from the Cape Town Longitudinal Cohort. Nine resting‐state networks were generated by ICA. Voxelwise group comparison between a combined FAS/PFAS group and controls revealed localized dose‐dependent functional connectivity reductions in five regions in separate networks: anterior default mode, salience, ventral and dorsal attention, and R executive control. The former three also showed lower connectivity in the HE group. Gray matter connectivity deficits in four of the five networks appear to be related to deficits in white matter tracts that provide intra‐RSN connections. Hum Brain Mapp 38:5217–5233, 2017. © 2017 Wiley Periodicals, Inc.