Altered resting‐state activity in seasonal affective disorder

At present, our knowledge about seasonal affective disorder (SAD) is based mainly up on clinical symptoms, epidemiology, behavioral characteristics and light therapy. Recently developed measures of resting‐state functional brain activity might provide neurobiological markers of brain disorders. Studying functional brain activity in SAD could enhance our understanding of its nature and possible treatment strategies. Functional network connectivity (measured using ICA‐dual regression), and amplitude of low‐frequency fluctuations (ALFF) were measured in 45 antidepressant‐free patients (39.78 ± 10.64, 30 ♀, 15 ♂) diagnosed with SAD and compared with age‐, gender‐ and ethnicity‐matched healthy controls (HCs) using resting‐state functional magnetic resonance imaging. After correcting for Type 1 error at high model orders (inter‐RSN correction), SAD patients showed significantly increased functional connectivity in 11 of the 47 identified RSNs. Increased functional connectivity involved RSNs such as visual, sensorimotor, and attentional networks. Moreover, our results revealed that SAD patients compared with HCs showed significant higher ALFF in the visual and right sensorimotor cortex. Abnormally altered functional activity detected in SAD supports previously reported attentional and psychomotor symptoms in patients suffering from SAD. Further studies, particularly under task conditions, are needed in order to specifically investigate cognitive deficits in SAD. Hum Brain Mapp 35:161–172, 2014. © 2012 Wiley Periodicals, Inc.     

Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain

During rest, multiple cortical brain regions are functionally linked forming resting‐state networks. This high level of functional connectivity within resting‐state networks suggests the existence of direct neuroanatomical connections between these functionally linked brain regions to facilitate the ongoing interregional neuronal communication. White matter tracts are the structural highways of our brain, enabling information to travel quickly from one brain region to another region. In this study, we examined both the functional and structural connections of the human brain in a group of 26 healthy subjects, combining 3 Tesla resting‐state functional magnetic resonance imaging time‐series with diffusion tensor imaging scans. Nine consistently found functionally linked resting‐state networks were retrieved from the resting‐state data. The diffusion tensor imaging scans were used to reconstruct the white matter pathways between the functionally linked brain areas of these resting‐state networks. Our results show that well‐known anatomical white matter tracts interconnect at least eight of the nine commonly found resting‐state networks, including the default mode network, the core network, primary motor and visual network, and two lateralized parietal‐frontal networks. Our results suggest that the functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.     

Regression‐based machine‐learning approaches to predict task activation using resting‐state fMRI

Resting‐state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting‐state network features to activation z‐scores. The question remains whether the relatively simplistic GLM is the best approach to accomplish this prediction. In this study, several regression‐based machine‐learning approaches were compared, including GLMs, feed‐forward neural networks, and random forest bootstrap aggregation (bagging). Resting‐state and task data from 350 Human Connectome Project subjects were analyzed. First, the effect of the number of training subjects on the prediction accuracy was evaluated. In addition, the prediction accuracy and Dice coefficient were compared across models. Prediction accuracy increased with the training number up to 200 subjects; however, an elbow in the prediction curve occurred around 30–40 training subjects. All models performed well with correlation matrices, which displayed correlation between actual and predicted task activation for all subjects, exhibiting a strong diagonal trend for all tasks. Overall, the neural network and random forest bagging techniques outperformed the GLM. These approaches, however, require additional computing power and processing time. These results show that, while the GLM performs well, resting‐state fMRI prediction of task activation could benefit from more complex machine learning approaches.     

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 functional connectivity and motor imagery brain activation

Motor imagery (MI) relies on the mental simulation of an action without any overt motor execution (ME), and can facilitate motor learning and enhance the effect of rehabilitation in patients with neurological conditions. While functional magnetic resonance imaging (fMRI) during MI and ME reveals shared cortical representations, the role and functional relevance of the resting‐state functional connectivity (RSFC) of brain regions involved in MI is yet unknown. Here, we performed resting‐state fMRI followed by fMRI during ME and MI with the dominant hand. We used a behavioral chronometry test to measure ME and MI movement duration and compute an index of performance (IP). Then, we analyzed the voxel‐matched correlation between the individual MI parameter estimates and seed‐based RSFC maps in the MI network to measure the correspondence between RSFC and MI fMRI activation. We found that inter‐individual differences in intrinsic connectivity in the MI network predicted several clusters of activation. Taken together, present findings provide first evidence that RSFC within the MI network is predictive of the activation of MI brain regions, including those associated with behavioral performance, thus suggesting a role for RSFC in obtaining a deeper understanding of neural substrates of MI and of MI ability. Hum Brain Mapp 37:3847–3857, 2016. © 2016 Wiley Periodicals, Inc.     

Linking resting‐state networks and social cognition in schizophrenia and bipolar disorder

Individuals with schizophrenia and bipolar disorder show alterations in functional neural connectivity during rest. However, resting‐state network (RSN) disruptions have not been systematically compared between the two disorders. Further, the impact of RSN disruptions on social cognition, a key determinant of functional outcome, has not been studied. Forty‐eight individuals with schizophrenia, 46 with bipolar disorder, and 48 healthy controls completed resting‐state functional magnetic resonance imaging. An atlas‐based approach was used to examine functional connectivity within nine RSNs across the cortex. RSN connectivity was assessed via nonparametric permutation testing, and associations with performance on emotion perception, mentalizing, and emotion management tasks were examined. Group differences were observed in the medial and lateral visual networks and the sensorimotor network. Individuals with schizophrenia demonstrated reduced connectivity relative to healthy controls in all three networks. Individuals with bipolar disorder demonstrated reduced connectivity relative to controls in the medial visual network and connectivity within this network was significantly positively correlated with emotion management. In healthy controls, connectivity within the medial and lateral visual networks positively correlated with mentalizing. No significant correlations were found for either visual network in schizophrenia. Results highlight the role of altered early visual processing in social cognitive deficits in both schizophrenia and bipolar disorder. However, individuals with bipolar disorder appear to compensate for disrupted visual network connectivity on social cognitive tasks, whereas those with schizophrenia do not. The current study adds clarity on the neurophysiology underlying social cognitive deficits that result in impaired functioning in serious mental illness.     

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.     

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.     

Resting‐state brain fluctuation and functional connectivity dissociate moral injury from posttraumatic stress disorder

Moral injury is closely associated with posttraumatic stress disorder (PTSD) and characterized by disturbances in social and moral cognition. Little is known about the neural underpinnings of moral injury, and whether the neural correlates are different between moral injury and PTSD. A sample of 26 U.S. military veterans (two females: 28–55 years old) were investigated to determine how subjective appraisals of morally injurious events measured by Moral Injury Event Scale (MIES) and PTSD symptoms are differentially related to spontaneous fluctuations indexed by amplitude of low frequency fluctuation (ALFF) as well as functional connectivity during resting‐state functional magnetic resonance imaging scanning. ALFF in the left inferior parietal lobule (L‐IPL) was positively associated with MIES subscores of transgressions, negatively associated with subscores of betrayals, and not related with PTSD symptoms. Moreover, functional connectivity between the L‐IPL and bilateral precuneus was positively related with PTSD symptoms and negatively related with MIES total scores. Our results provide the first evidence that morally injurious events and PTSD symptoms have dissociable neural underpinnings, and behaviorally distinct subcomponents of morally injurious events are different in neural responses. The findings increase our knowledge of the neural distinctions between moral injury and PTSD and may contribute to developing nosology and interventions for military veterans afflicted by moral injury.     

Real‐time presurgical resting‐state fMRI in patients with brain tumors: Quality control and comparison with task‐fMRI and intraoperative mapping

Resting‐state functional magnetic resonance imaging (rsfMRI) is a promising task‐free functional imaging approach, which may complement or replace task‐based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real‐time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)‐echo planar imaging (EPI) with repetition time: 400 ms. Moving‐averaged sliding‐window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting‐state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement‐related false‐positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting‐state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task‐activation in motor cortex, Broca's, and Wernicke's areas was 5–10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real‐time high‐speed rsfMRI for presurgical mapping of eloquent cortex with real‐time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.     

Improved correspondence of resting‐state networks after macroanatomical alignment

Resting state brain activity, as measured with functional magnetic resonance imaging (fMRI) in the absence of stimulation, is widely investigated in clinical, pharmacological, developmental and cross‐species neuroscience research. However, despite the general and broad interest in understating the nature of resting state networks (RSNs), there has not been a thorough investigation into the relationship between these functional networks and their adherence to underling brain anatomy. We acquired resting state fMRI data from 10 subjects and extracted individual and group RSN maps respectively using independent component analysis (ICA) and self organising group‐level ICA (sogICA). Cortex based alignment (CBA), an advanced surface based alignment technique which uses individual curvature information to align individual subjects' brains to a dynamic group average, was used to maximise anatomical correspondence across subjects. Cross subject spatial correlations of the RSN maps (independent components) were carried out with and without CBA. Seven RSNs, which are amongst the most reported and studied networks, were identified. We observed a systematic gain in the spatial correlation in all of them following CBA, although this gain was not uniform across RSNs. The observed increase in similarity of the functional RSNs after anatomical alignment illustrates that these functional networks are indeed related to underlying macroanatomical features. Moreover, our results demonstrate that by correcting for individual anatomical differences, advanced surface based alignment techniques increase the overlap of corresponding resting state networks across subjects, thereby providing a useful means to improve resting state group statistics with no need for substantial smoothing. Hum Brain Mapp 35:673–682, 2014. © 2012 Wiley Periodicals, Inc.     

Maternal brain resting‐state connectivity in the postpartum period

In the postpartum period, the maternal brain experiences both structural and functional plasticity. Although we have a growing understanding of the responses of the human maternal brain to infant stimuli, little is known about the intrinsic connectivity among those regions during the postpartum months. Resting‐state functional connectivity (rsFC) provides a measure of the functional architecture of the brain based upon intrinsic functional connectivity (ie, the temporal correlation in blood oxygenation level dependent signal when the brain is not engaged in a specific task). In the present study, we used resting‐state functional magnetic resonance imaging to examine how later postpartum months are associated with rsFC and maternal behaviours. We recruited a sample of 47 socioeconomically diverse first‐time mothers with singleton pregnancies. Because the amygdala has been shown to play a critical role in maternal behaviours in the postpartum period, this was chosen as the seed for a seed‐based correlation analysis. For the left amygdala, later postpartum months were associated with greater connectivity with the anterior cingulate gyrus, left nucleus accumbens, right caudate and left cerebellum (P < 0.05, false discovery rate corrected). Furthermore, in an exploratory analysis, we observed indications that rsFC between the left amygdala and left nucleus accumbens was positively associated with maternal structuring during a mother child‐interaction. In addition, later postpartum months were associated with greater connectivity between the right amygdala and the bilateral caudate and right putamen. Overall, we provide evidence of relationships between postpartum months and rsFC in the regions involved in salience detection and regions involved in maternal motivation. Greater connectivity between the amygdala and nucleus accumbens may play a role in positive maternal behaviours.     

The effects of antidepressant treatment on resting‐state functional brain networks in patients with major depressive disorder

Although most knowledge regarding antidepressant effects is at the receptor level, the neurophysiological correlates of these neurochemical changes remain poorly understood. Such an understanding could benefit from elucidation of antidepressant effects at the level of neural circuits, which would be crucial in identifying biomarkers for monitoring treatment efficacy of antidepressants. In this study, we recruited 20 first‐episode drug‐naive major depressive disorder (MDD) patients and performed resting‐state functional magnetic resonance imaging (MRI) scans before and after 8 weeks of treatment with a selective serotonin reuptake inhibitor—escitalopram. Twenty healthy controls (HCs) were also scanned twice with an 8‐week interval. Whole‐brain connectivity was analyzed using a graph‐theory approach—functional connectivity strength (FCS). The analysis of covariance of FCS was used to determine treatment‐related changes. We observed significant group‐by‐time interaction on FCS in the bilateral dorsomedial prefrontal cortex and bilateral hippocampi. Post hoc analyses revealed that the FCS values in the bilateral dorsomedial prefrontal cortex were significantly higher in the MDD patients compared to HCs at baseline and were significantly reduced after treatment; conversely, the FCS values in the bilateral hippocampi were significantly lower in the patients at baseline and were significantly increased after treatment. Importantly, FCS reduction in the dorsomedial prefrontal cortex was significantly correlated with symptomatic improvement. Together, these findings provided evidence that this commonly used antidepressant can selectively modulate the intrinsic network connectivity associated with the medial prefrontal‐limbic system, thus significantly adding to our understanding of antidepressant effects at a circuit level and suggesting potential imaging‐based biomarkers for treatment evaluation in MDD. Hum Brain Mapp 36:768–778, 2015. © 2014 Wiley Periodicals, Inc.     

Brain network connectivity–behavioral relationships exhibit trait‐like properties: Evidence from hippocampal connectivity and memory

Despite a growing number of studies showing relationships between behavior and resting‐state functional MRI measures of large‐scale brain network connectivity, no study to our knowledge has sought to investigate whether intrinsic connectivity–behavioral relationships are stable over time. In this study, we investigated the stability of such brain–behavior relationships at two timepoints, approximately 1 week apart. We focused on the relationship between the strength of hippocampal connectivity to posterior cingulate cortex and episodic memory performance. Our results showed that this relationship is stable across samples of a different age and reliable over two points in time. These findings provide the first evidence that the relationship between large‐scale intrinsic network connectivity and episodic memory performance is a stable characteristic that varies between individuals. © 2015 Wiley Periodicals, Inc.     

Predicting episodic and spatial memory performance from hippocampal resting‐state functional connectivity: Evidence for an anterior–posterior division of function

fMRI studies have identified distinct resting‐state functional connectivity (RSFC) networks associated with the anterior and posterior hippocampus. However, the functional relevance of these two networks is still largely unknown. Hippocampal lesion studies and task‐related fMRI point to a role for the anterior hippocampus in nonspatial episodic memory and the posterior hippocampus in spatial memory. We used Relevance Vector Regression (RVR), a machine‐learning method that enables predictions of continuous outcome measures from multivariate patterns of brain imaging data, to test the hypothesis that patterns of whole‐brain RSFC associated with the anterior hippocampus predict episodic memory performance, while patterns of whole‐brain RSFC associated with the posterior hippocampus predict spatial memory performance. Magnetic resonance imaging and memory assessment took place at two separate occasions. The anterior and posterior RSFC largely corresponded with previous findings, and showed no effect of laterality. Supporting the hypothesis, RVR produced accurate predictions of episodic performance from anterior, but not posterior, RSFC, and accurate predictions of spatial performance from posterior, but not anterior, RSFC. In contrast, a univariate approach could not predict performance from resting‐state connectivity. This supports a functional dissociation between the anterior and posterior hippocampus, and indicates a multivariate relationship between intrinsic functional networks and cognitive performance within specific domains, that is relatively stable over time.     

Cognitive impairment and resting‐state network connectivity in Parkinson's disease

The purpose of this work was to evaluate changes in the connectivity patterns of a set of cognitively relevant, dynamically interrelated brain networks in association with cognitive deficits in Parkinson's disease (PD) using resting‐state functional MRI. Sixty‐five nondemented PD patients and 36 matched healthy controls were included. Thirty‐four percent of PD patients were classified as having mild cognitive impairment (MCI) based on performance in attention/executive, visuospatial/visuoperceptual (VS/VP) and memory functions. A data‐driven approach using independent component analysis (ICA) was used to identify the default‐mode network (DMN), the dorsal attention network (DAN) and the bilateral frontoparietal networks (FPN), which were compared between groups using a dual‐regression approach controlling for gray matter atrophy. Additional seed‐based analyses using a priori defined regions of interest were used to characterize local changes in intranetwork and internetwork connectivity. Structural group comparisons through voxel‐based morphometry and cortical thickness were additionally performed to assess associated gray matter atrophy. ICA results revealed reduced connectivity between the DAN and right frontoinsular regions in MCI patients, associated with worse performance in attention/executive functions. The DMN displayed increased connectivity with medial and lateral occipito‐parietal regions in MCI patients, associated with worse VS/VP performance, and with occipital reductions in cortical thickness. In line with data‐driven results, seed‐based analyses mainly revealed reduced within‐DAN, within‐DMN and DAN‐FPN connectivity, as well as loss of normal DAN‐DMN anticorrelation in MCI patients. Our findings demonstrate differential connectivity changes affecting the networks evaluated, which we hypothesize to be related to the pathophysiological bases of different types of cognitive impairment in PD. Hum Brain Mapp, 36:199–212, 2015. © 2014 Wiley Periodicals, Inc.     

Cortical laminar resting‐state signal fluctuations scale with the hypercapnic blood oxygenation level‐dependent response

Calibrated functional magnetic resonance imaging can remove unwanted sources of signal variability in the blood oxygenation level‐dependent (BOLD) response. This is achieved by scaling, using information from a perfusion‐sensitive scan during a purely vascular challenge, typically induced by a gas manipulation or a breath‐hold task. In this work, we seek for a validation of the use of the resting‐state fluctuation amplitude (RSFA ) as a scaling factor to remove vascular contributions from the BOLD response. Given the peculiarity of depth‐dependent vascularization in gray matter, BOLD and vascular space occupancy (VASO) data were acquired at submillimeter resolution and averaged across cortical laminae. RSFA from the primary motor cortex was, thus, compared to the amplitude of hypercapnia‐induced signal changes (tSD_hc ) and with the M factor of the Davis model on a laminar level. High linear correlations were observed for RSFA and tSD_hc ( R² = 0.92 ± 0.06 ) and somewhat reduced for RSFA and M ( R² = 0.62 ± 0.19 ). Laminar profiles of RSFA ‐normalized BOLD signal changes yielded good agreement with corresponding VASO profiles. Overall, this suggests that RSFA contains strong vascular components and is also modulated by baseline quantities contained in the M factor. We conclude that RSFA may replace the scaling factor tSD_hc for normalizing the laminar BOLD response.     

Corticostriatal connectivity fingerprints: Probability maps based on resting‐state functional connectivity

Over the last decade, structure–function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting‐state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice‐similarity values 0.40–1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non‐human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478–1491, 2017. © 2016 Wiley Periodicals, Inc.     

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.