Less head motion during MRI under task than resting-state conditions.
Neuroimage. 2016 Dec 02;:
Authors: Huijbers W, Van Dijk KR, Boenniger MM, Stirnberg R, Breteler MM
Head motion reduces data quality of neuroimaging data. In three functional magnetic resonance imaging (MRI) experiments we demonstrate that people make less head movements under task than resting-state conditions. In Experiment 1, we observed less head motion during a memory encoding task than during the resting-state condition. In Experiment 2, using publicly shared data from the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study, we again found less head motion during several active task conditions than during a resting-state condition, although some task conditions also showed comparable motion. In the healthy controls, we found more head motion in men than in women and more motion with increasing age. When comparing clinical groups, we found that patients with a clinical diagnosis of bipolar disorder, or schizophrenia, move more compared to healthy controls or patients with ADHD. Both these experiments had a fixed acquisition order across participants, and we could not rule out that a first or last scan during a session might be particularly prone to more head motion. Therefore, we conducted Experiment 3, in which we collected several task and resting-state fMRI runs with an acquisition order counter-balanced. The results of Experiment 3 show again less head motion during several task conditions than during rest. Together these experiments demonstrate that small head motions occur during MRI even with careful instruction to remain still and fixation with foam pillows, but that head motion is lower when participants are engaged in a cognitive task. These finding may inform the choice of functional runs when studying difficult-to-scan populations, such as children or certain patient populations. Our findings also indicate that differences in head motion complicate direct comparisons of measures of functional neuronal networks between task and resting-state fMRI because of potential differences in data quality. In practice, a task to reduce head motion might be especially useful when acquiring structural MRI data such as T1/T2-weighted and diffusion MRI in research and clinical settings.
PMID: 27919751 [PubMed - as supplied by publisher]
Mapping the functional connectome in traumatic brain injury: What can graph metrics tell us?
Neuroimage. 2016 Dec 02;:
Authors: Caeyenberghs K, Verhelst H, Clemente A, Wilson PH
OBJECTIVE: Traumatic brain injury (TBI) is associated with cognitive and motor deficits, and poses a significant personal, societal, and economic burden. One mechanism by which TBI is thought to affect cognition and behaviour is through changes in functional connectivity. Graph theory is a powerful framework for quantifying topological features of neuroimaging-derived functional networks. The objective of this paper is to review studies examining functional connectivity in TBI with an emphasis on graph theoretical analysis that is proving to be valuable in uncovering network abnormalities in this condition.
METHODS: We review studies that have examined TBI-related alterations in different properties of the functional brain network, including global integration, segregation, centrality and resilience. We focus on functional data using task-related fMRI or resting state fMRI in patients with TBI of different severity and recovery phase, and consider how graph metrics may inform rehabilitation and enhance efficacy. Moreover, we outline some methodological challenges associated with the examination of functional connectivity in patients with brain injury, including the sample size, parcellation scheme used, node definition and subgroup analyses.
RESULTS: The findings suggest that TBI is associated with hyperconnectivity and a suboptimal global integration, characterized by increased connectivity degree and strength and reduced efficiency of functional networks. This altered functional connectivity, also evident in other clinical populations, is attributable to diffuse white matter pathology and reductions in gray and white matter volume. These functional alterations are implicated in post-concussional symptoms, posttraumatic stress and neurocognitive dysfunction after TBI. Finally, the effects of focal lesions have been found to depend critically on topological position and their role in the network.
CONCLUSION: Graph theory is a unique and powerful tool for exploring functional connectivity in brain-injured patients. One limitation is that its results do not provide specific measures about the biophysical mechanism underlying TBI. Continued work in this field will hopefully see graph metrics used as biomarkers to provide more accurate diagnosis and help guide treatment at the individual patient level.
PMID: 27919750 [PubMed - as supplied by publisher]
The role of anxiety in stuttering: evidence from functional connectivity.
Neuroscience. 2016 Dec 02;:
Authors: Yang Y, Jia F, Ting Siok W, Hai Tan L
Persistent developmental stuttering is a neurologically based speech disorder associated with cognitive-linguistic, motor and emotional abnormalities. Previous studies investigating the relationship between anxiety and stuttering have yielded mixed results, but it has not yet been examined whether anxiety influences brain activity underlying stuttering. Here, by using functional magnetic resonance imaging (fMRI), we investigated the functional connectivity associated with state anxiety in a syllable repetition task, and trait anxiety during rest in adults who stutter (N=19) and fluent controls (N=19). During the speech task, people who stutter (PWS) showed increased functional connectivity of the right amygdala with the prefrontal gyrus (the left ventromedial frontal gyrus and right middle frontal gyrus) and the left insula compared to controls. During rest, PWS showed stronger functional connectivity between the right hippocampus and the left orbital frontal gyrus, and between the left hippocampus and left motor areas than controls. Taken together, our results suggest aberrant bottom-up and/or top-down interactions for anxiety regulation, which might be responsible for the higher level of state anxiety during speech and for the anxiety-prone trait in PWS. To our knowledge, this is the first study to examine the neural underpinnings of anxiety in PWS, thus yielding new insight in the causes of stuttering which might aid strategies for the diagnosis and treatment of stuttering.
PMID: 27919696 [PubMed - as supplied by publisher]
Resting-state connectivity biomarkers define neurophysiological subtypes of depression.
Nat Med. 2016 Dec 05;:
Authors: Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, Pascual-Leone A, Voss HU, Casey BJ, Dubin MJ, Liston C
Biomarkers have transformed modern medicine but remain largely elusive in psychiatry, partly because there is a weak correspondence between diagnostic labels and their neurobiological substrates. Like to other neuropsychiatric disorders, depression is not a unitary disease, but rather a heterogeneous syndrome that encompasses varied, co-occurring symptoms and divergent responses to treatment. By using functional magnetic resonance imaging (fMRI) in a large multisite sample (n = 1,188), we show here that patients with depression can be subdivided into four neurophysiological subtypes ('biotypes') defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks. Clustering patients on this basis enabled the development of diagnostic classifiers (biomarkers) with high (82-93%) sensitivity and specificity for depression subtypes in multisite validation (n = 711) and out-of-sample replication (n = 477) data sets. These biotypes cannot be differentiated solely on the basis of clinical features, but they are associated with differing clinical-symptom profiles. They also predict responsiveness to transcranial-magnetic-stimulation therapy (n = 154). Our results define novel subtypes of depression that transcend current diagnostic boundaries and may be useful for identifying the individuals who are most likely to benefit from targeted neurostimulation therapies.
PMID: 27918562 [PubMed - as supplied by publisher]
Distinct intrinsic functional brain network abnormalities in methamphetamine-dependent patients with and without a history of psychosis.
Addict Biol. 2016 Dec 05;:
Authors: Ipser JC, Uhlmann A, Taylor P, Harvey BH, Wilson D, Stein DJ
Chronic methamphetamine use is associated with executive functioning deficits that suggest dysfunctional cognitive control networks (CCNs) in the brain. Likewise, abnormal connectivity between intrinsic CCNs and default mode networks (DMNs) has also been associated with poor cognitive function in clinical populations. Accordingly, we tested the extent to which methamphetamine use predicts abnormal connectivity between these networks, and whether, as predicted, these abnormalities are compounded in patients with a history of methamphetamine-associated psychosis (MAP). Resting-state fMRI data were acquired from 46 methamphetamine-dependent patients [19 with MAP, 27 without (MD)], as well as 26 healthy controls (CTRL). Multivariate network modelling and whole-brain voxel-wise connectivity analyses were conducted to identify group differences in intrinsic connectivity across four cognitive control and three DMN networks identified using an independent components analysis approach (meta-ICA). The relationship of network connectivity and psychotic symptom severity, as well as antipsychotic treatment and methamphetamine use variables, was also investigated. Robust evidence of hyper-connectivity was observed between the right frontoparietal and anterior DMN networks in MAP patients, and 'normalized' with increased duration of treatment with antipsychotics. Attenuation of anticorrelated anterior DMN-dorsal attention network activity was also restricted to this group. Elevated coupling detected in MD participants between anterior and posterior DMN networks became less apparent with increasing duration of abstinence from methamphetamine. In summary, we observed both alterations of RSN connectivity between DMN networks with chronic methamphetamine exposure, as well as DMN-CCN coupling abnormalities consistent with possible MAP-specific frontoparietal deficits in the biasing of task-appropriate network activity.
PMID: 27917569 [PubMed - as supplied by publisher]
Aberrant regional homogeneity in Parkinson's disease: A voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies.
Neurosci Biobehav Rev. 2016 Dec 01;:
Authors: Pan P, Zhan H, Xia M, Zhang Y, Guan D, Xu Y
Studies of abnormal regional homogeneity (ReHo) in Parkinson's disease (PD) have reported inconsistent results. Therefore, we conducted a meta-analysis using the Seed-based d Mapping software package to identify the most consistent and replicable findings. A systematic literature search was performed to identify eligible whole-brain resting-state functional magnetic resonance imaging studies that had measured differences in ReHo between patients with PD and healthy controls between January 2000 and June 4, 2016. A total of ten studies reporting 11 comparisons (212 patients; 182 controls) were included. Increased ReHo was consistently identified in the bilateral inferior parietal lobules, bilateral medial prefrontal cortices, and left cerebellum of patients with PD when compared to healthy controls, while decreased ReHo was observed in the right putamen, right precentral gyrus, and left lingual gyrus. The results of the current meta-analysis demonstrate a consistent and coexistent pattern of impairment and compensation of intrinsic brain activity that predominantly involves the default mode and motor networks, which may advance our understanding of the pathophysiological mechanisms underlying PD.
PMID: 27916710 [PubMed - as supplied by publisher]
Directed functional connectivity of posterior cingulate cortex and whole brain in Alzheimer's disease and mild cognitive impairment.
Curr Alzheimer Res. 2016 Dec 01;
Authors: Yu E, Liao Z, Mao D, Zhang Q, Ji G, Li Y, Ding Z
BACKGROUND: Impaired functional connectivity in the default mode network (DMN) is supposedly involved in Alzheimer's disease (AD) progression. The posterior cingulate cortex (PCC) might be an imaging marker for monitoring AD progression.
OBJECTIVE: To investigate the alterations in the directed functional connectivity between the PCC and whole brain in patients with AD, patients with mild cognitive impairment (MCI), and healthy controls.
METHODS: A total of 116 enrolled participants were divided into three groups: AD (n=32), MCI (n=26), and controls (n=58). Using resting-state functional magnetic resonance imaging (rs-fMRI), the directed functional connectivity was studied using Granger causality analysis (GCA).
RESULTS: Almost all of the directed functional connections with significant differences were unidirectional. Compared with the NC group, the AD group showed enhanced directed connectivity from the whole brain to the PCC mainly for regions outside the DMN, and reduced connectivity from the PCC to the whole brain mainly for regions inside the DMN. Compared with the NC group, the MCI group showed enhanced directed connectivity from the PCC to the whole brain for the bilateral precuneus and postcentralgyrus, and reduced connectivity from the whole brain to the PCC for regions outside the DMN. Compared with the MCI group, the abnormal directed connectivity in the AD group was predominantly in the left hemisphere, possibly suggesting asymmetric characteristics.
CONCLUSION: In patients with AD, the PCC in the DMN shows disorders in receiving and transmitting information, and these abnormalities are directional.
PMID: 27915993 [PubMed - as supplied by publisher]
Predicting Individual Brain Functional Connectivity Using a Bayesian Hierarchical Model.
Neuroimage. 2016 Nov 30;:
Authors: Dai T, Guo Y, Alzheimer's Disease Neuroimaging Initiative
Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual functional connectivity. We show there exists a nice connection between our proposed estimator and a recently developed shrinkage estimator of connectivity measures in the neuroimaging community. We develop an expectation-maximization (EM) algorithm for estimation of the proposed Bayesian hierarchical model. Simulations studies are performed to evaluate the accuracy of our proposed prediction methods. We illustrate the application of the methods with two data examples: the longitudinal resting-state fMRI from ADNI2 study and the test-retest fMRI data from Kirby21 study. In both the simulation studies and the fMRI data applications, we demonstrate that the proposed methods provide more accurate prediction and more reliable estimation of individual functional connectivity as compared with alternative methods.
PMID: 27915121 [PubMed - as supplied by publisher]
Heritability of the Effective Connectivity in the Resting-State Default Mode Network.
Cereb Cortex. 2016 Nov 23;
Authors: Xu J, Yin X, Ge H, Han Y, Pang Z, Liu B, Liu S, Friston K
The default mode network (DMN) is thought to reflect endogenous neural activity, which is considered as one of the most intriguing phenomena in cognitive neuroscience. Previous studies have found that key regions within the DMN are highly interconnected. Here, we characterized the genetic influences on causal or directed information flow within the DMN during the resting state. In this study, we recruited 46 pairs of twins and collected fMRI imaging data using a 3.0 T scanner. Dynamic causal modeling was conducted for each participant, and a structural equation model was used to calculate the heritability of DMN in terms of its effective connectivity. Model comparison favored a full-connected model. Structural equal modeling was used to estimate the additive genetics (A), common environment (C) and unique environment (E) contributions to variance for the DMN effective connectivity. The ACE model was preferred in the comparison of structural equation models. Heritability of DMN effective connectivity was 0.54, suggesting that the genetic made a greater contribution to the effective connectivity within DMN. Establishing the heritability of default-mode effective connectivity endorses the use of resting-state networks as endophenotypes or intermediate phenotypes in the search for the genetic basis of psychiatric or neurological illnesses.
PMID: 27913429 [PubMed - as supplied by publisher]
sGraSP: a Graph-based Method for the Derivation of Subject-specific Functional Parcellations of the Brain.
J Neurosci Methods. 2016 Nov 29;:
Authors: Honnorat N, Satterthwaite TD, Gur RE, Gur RC, Davatzikos C
BACKGROUND: Resting-state fMRI (rs-fMRI) has emerged as a prominent tool for the study of functional connectivity. The identification of the regions associated with the different brain functions has received significant interest. However, most of the studies conducted so far have focused on the definition of a common set of regions, valid for an entire population. The variation of the functional regions within a population has rarely been accounted for. New Method: In this paper, we propose sGraSP, a graph-based approach for the derivation of subject-specific functional parcellations. Our method generates first a common parcellation for an entire population, which is then adapted to each subject individually.
RESULTS: Several cortical parcellations were generated for 859 children being part of the Philadelphia Neurodevelopmental Cohort. The stability of the parcellations generated by sGraSP was tested by mixing population and subject rs-fMRI signals, to generate subject-specific parcels increasingly closer to the population parcellation. We also checked if the parcels generated by our method were better capturing a development trend underlying our data than the original parcels, defined for the entire population. Comparison with Existing Methods: We compared sGraSP with a simpler and faster approach based on a Voronoi tessellation, by measuring their ability to produce functionally coherent parcels adapted to the subject data.
CONCLUSIONS: Our parcellations outperformed the Voronoi tessellations. The parcels generated by sGraSP vary consistently with respect to signal mixing, the results are highly reproducible and the neurodevelopmental trend is better captured with the subject-specific parcellation, under all the signal mixing conditions.
PMID: 27913211 [PubMed - as supplied by publisher]
Further evidence of alerted default network connectivity and association with theory of mind ability in schizophrenia.
Schizophr Res. 2016 Nov 29;:
Authors: Mothersill O, Tangney N, Morris DW, McCarthy H, Frodl T, Gill M, Corvin A, Donohoe G
BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) has repeatedly shown evidence of altered functional connectivity of large-scale networks in schizophrenia. The relationship between these connectivity changes and behaviour (e.g. symptoms, neuropsychological performance) remains unclear.
METHODS: Functional connectivity in 27 patients with schizophrenia or schizoaffective disorder, and 25 age and gender matched healthy controls was examined using rs-fMRI. Based on seed regions from previous studies, we examined functional connectivity of the default, cognitive control, affective and attention networks. Effects of symptom severity and theory of mind performance on functional connectivity were also examined.
RESULTS: Patients showed increased connectivity between key nodes of the default network including the precuneus and medial prefrontal cortex compared to controls (p<0.01, FWE-corrected). Increasing positive symptoms and increasing theory of mind performance were both associated with altered connectivity of default regions within the patient group (p<0.01, FWE-corrected).
DISCUSSION: This study confirms previous findings of default hyper-connectivity in schizophrenia spectrum patients and reveals an association between altered default connectivity and positive symptom severity. As a novel find, this study also shows that default connectivity is correlated to and predictive of theory of mind performance. Extending these findings by examining the effects of emerging social cognition treatments on both default connectivity and theory of mind performance is now an important goal for research.
PMID: 27913157 [PubMed - as supplied by publisher]
Changes in resting-state connectivity in musicians with embouchure dystonia.
Mov Disord. 2016 Dec 02;:
Authors: Haslinger B, Noé J, Altenmüller E, Riedl V, Zimmer C, Mantel T, Dresel C
OBJECTIVE: Embouchure dystonia is a highly disabling task-specific dystonia in professional brass musicians leading to spasms of perioral muscles while playing the instrument. As they are asymptomatic at rest, resting-state functional magnetic resonance imaging in these patients can reveal changes in functional connectivity within and between brain networks independent from dystonic symptoms.
METHODS: We therefore compared embouchure dystonia patients to healthy musicians with resting-state functional magnetic resonance imaging in combination with independent component analyses.
RESULTS: Patients showed increased functional connectivity of the bilateral sensorimotor mouth area and right secondary somatosensory cortex, but reduced functional connectivity of the bilateral sensorimotor hand representation, left inferior parietal cortex, and mesial premotor cortex within the lateral motor function network. Within the auditory function network, the functional connectivity of bilateral secondary auditory cortices, right posterior parietal cortex and left sensorimotor hand area was increased, the functional connectivity of right primary auditory cortex, right secondary somatosensory cortex, right sensorimotor mouth representation, bilateral thalamus, and anterior cingulate cortex was reduced. Negative functional connectivity between the cerebellar and lateral motor function network and positive functional connectivity between the cerebellar and primary visual network were reduced.
CONCLUSIONS: Abnormal resting-state functional connectivity of sensorimotor representations of affected and unaffected body parts suggests a pathophysiological predisposition for abnormal sensorimotor and audiomotor integration in embouchure dystonia. Altered connectivity to the cerebellar network highlights the important role of the cerebellum in this disease. © 2016 International Parkinson and Movement Disorder Society.
PMID: 27911020 [PubMed - as supplied by publisher]
Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal.
Neuroimage. 2016 Nov 28;:
Authors: Gao YR, Ma Y, Zhang Q, Winder AT, Liang Z, Antinori L, Drew PJ, Zhang N
Functional magnetic resonance imaging (fMRI) has allowed the noninvasive study of task-based and resting-state brain dynamics in humans by inferring neural activity from blood-oxygenation-level dependent (BOLD) signal changes. An accurate interpretation of the hemodynamic changes that underlie fMRI signals depends on the understanding of the quantitative relationship between changes in neural activity and changes in cerebral blood flow, oxygenation and volume. While there has been extensive study of neurovascular coupling in anesthetized animal models, anesthesia causes large disruptions of brain metabolism, neural responsiveness and cardiovascular function. Here, we review work showing that neurovascular coupling and brain circuit function in the awake animal are profoundly different from those in the anesthetized state. We argue that the time is right to study neurovascular coupling and brain circuit function in the awake animal to bridge the physiological mechanisms that underlie animal and human neuroimaging signals, and to interpret them in light of underlying neural mechanisms. Lastly, we discuss recent experimental innovations that have enabled the study of neurovascular coupling and brain-wide circuit function in un-anesthetized and behaving animal models.
PMID: 27908788 [PubMed - as supplied by publisher]
Cerebral blood flow measured by arterial spin labeling MRI at resting state in normal aging and Alzheimer's disease.
Neurosci Biobehav Rev. 2016 Nov 28;:
Authors: Zhang N, Gordon ML, Goldberg TE
Arterial spin labeling (ASL) magnetic resonance imaging uses arterial blood water as an endogenous tracer to measure cerebral blood flow (CBF). In this review, based on ASL studies in the resting state, we discuss state-of-the-art technical and data processing improvements in ASL, and ASL CBF changes in normal aging, mild cognitive impairment (MCI), Alzheimer's disease (AD), and other types of dementia. We propose that vascular and AD risk factors should be considered when evaluating CBF changes in aging, and that other validated biomarkers should be used as inclusion criteria or covariates when evaluating CBF changes in MCI and AD. With improvements in hardware and experimental design, ASL is proving to be an increasingly promising tool for exploring pathogenetic mechanisms, early detection, monitoring disease progression and pharmacological response, and differential diagnosis of AD.
PMID: 27908711 [PubMed - as supplied by publisher]
Amplitude of Low Frequency Fluctuation (ALFF) in the Cervical Spinal Cord with Stenosis: A Resting State fMRI Study.
PLoS One. 2016;11(12):e0167279
Authors: Liu X, Qian W, Jin R, Li X, Luk KD, Wu EX, Hu Y
Cervical spondylotic myelopathy (CSM) is a common spinal cord dysfunction disease with complex symptoms in clinical presentation. Resting state fMRI (rsfMRI) has been introduced to study the mechanism of neural development of CSM. However, most of those studies focused on intrinsic functional connectivity rather than intrinsic regional neural activity level which is also frequently analyzed in rsfMRI studies. Thus, this study aims to explore whether the level of neural activity changes on the myelopathic cervical cord and evaluate the possible relationship between this change and clinical symptoms through amplitude of low frequency fluctuation (ALFF). Eighteen CSM patients and twenty five healthy subjects participated in rsfMRI scanning. ALFF was investigated on each patient and subject. The results suggested that ALFF values were higher in the CSM patients at all cervical segments, compared to the healthy controls. The severity of myelopathy was associated with the increase of ALFF. This finding would enrich our understanding on the neural development mechanism of CSM.
PMID: 27907060 [PubMed - in process]
The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition.
J Neurosci. 2016 Nov 30;36(48):12083-12094
Authors: Cohen JR, D'Esposito M
A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition.
SIGNIFICANCE STATEMENT: The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brain's ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.
PMID: 27903719 [PubMed - in process]
Formation of long-term locomotor memories is associated with functional connectivity changes in the cerebellar-thalamic-cortical network.
J Neurosci. 2016 Nov 30;:
Authors: Mawase F, Bar-Haim S, Shmuelof L
Although motor adaptation is typically rapid, accumulating evidence shows that it is also associated with long-lasting behavioural and neuronal changes. Two processes were suggested to explain the formation of long-term motor memories: recall -- reflecting a retrieval of previous motor actions, and faster relearning -- reflecting an increased sensitivity to errors. While these manifestations of motor memories were initially demonstrated in the context of adaptation experiments in reaching, indications of long-term motor memories were also recently demonstrated in other kinds of adaptation, such as in locomotor adaptation. Little is known about the neural processes that underlie these distinct aspects of memory. We hypothesize that recall and faster relearning reflect different learning processes that operate at the same time and depend on different neuronal networks. Seventeen subjects performed a multi-session locomotor adaptation experiment in the laboratory together with resting state and localizer fMRI scans following the baseline and the locomotor adaptation sessions. We report a modulation of the cerebellar-thalamic-cortical and cerebellar-basal ganglia networks following locomotor adaptation. Interestingly, while thalamic-cortical baseline connectivity was correlated with recall, cerebellar-thalamic baseline connectivity was correlated with faster relearning. Our results suggest that separate neuronal networks underlie error sensitivity and retrieval motor retention components. Individual differences in baseline resting-state connectivity can predict idiosyncratic combination of these components.
SIGNIFICANT STATEMENT: The ability to rapidly shape our motor behaviour in everyday activity, such as when walking on sand, suggests the existence of long-term motor memories. It was recently suggested that this ability is achieved by the retrieval of previous motor actions and by enhanced relearning capacity. Little is known about the neural mechanisms that underlie these memory processes. We study the modularity in long-term motor memories in the context of locomotor adaptation using resting state fMRI. We show that retrieval and relearning effects are associated with separate locomotor control networks, and that inter-subject variability in learning and in the generation of motor memories could be predicted from baseline resting-state connectivity in locomotor-related networks.
PMID: 27903707 [PubMed - as supplied by publisher]
Optimising experimental design for MEG resting state functional connectivity measurement.
Neuroimage. 2016 Nov 26;:
Authors: Liuzzi L, Gascoyne LE, Tewarie PK, Barratt EL, Boto E, Brookes MJ
The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of coregistration method and data recording duration. We show that the use of a foam head-cast, which is known to improve coregistration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies.
PMID: 27903441 [PubMed - as supplied by publisher]
Functional connectivity in the resting brain as biological correlate of the Affective Neuroscience Personality Scales.
Neuroimage. 2016 Nov 26;:
Authors: Deris N, Montag C, Reuter M, Weber B, Markett S
According to Jaak Panksepp's Affective Neuroscience Theory and the derived self-report measure, the Affective Neuroscience Personality Scales (ANPS), differences in the responsiveness of primary emotional systems form the basis of human personality. In order to investigate neuronal correlates of personality, the underlying neuronal circuits of the primary emotional systems were analyzed in the present fMRI-study by associating the ANPS to functional connectivity in the resting brain. N = 120 healthy participants were invited for the present study. The results were reinvestigated in an independent, smaller sample of N = 52 participants. A seed-based whole brain approach was conducted with seed-regions bilaterally in the basolateral and superficial amygdalae. The selection of seed-regions was based on meta-analytic data on affective processing and the Juelich histological atlas. Multiple regression analyses on the functional connectivity maps revealed associations with the SADNESS-scale in both samples. Functional resting-state connectivity between the left basolateral amygdala and a cluster in the postcentral gyrus, and between the right basolateral amygdala and clusters in the superior parietal lobe and subgyral in the parietal lobe was associated with SADNESS. No other ANPS-scale revealed replicable results. The present findings give first insights into the neuronal basis of the SADNESS-scale of the ANPS and support the idea of underlying neuronal circuits. In combination with previous research on genetic associations of the ANPS functional resting-state connectivity is discussed as a possible endophenotype of personality.
PMID: 27903439 [PubMed - as supplied by publisher]
[Fractional amplitude of low-frequency fluctuations in childhood and adolescence-onset schizophrenia: a resting state fMRI study].
Zhonghua Yi Xue Za Zhi. 2016 Nov 22;96(43):3479-3484
Authors: Lü D, Shao RR, Liang YH, Xia YH, Guo SQ
Objective: To explore the whole brain activity features of childhood and adolescence-onset schizophrenia using resting state fMRI. Methods: A total of 63 childhood and adolescence-onset schizophrenia patients (patients group), admitted to the second affiliated hospital of Xinxiang Medical University from October 2013 to October 2015 and fulfilled our inclusion criteria, and 39 healthy controls with age, sex and education matched (control group) were enrolled, then a resting-state fMRI scan was conducted for each participant. Fractional amplitude of low-frequency fluctuations (fALFF) approach was used to explore the differences of resting-state brain function between patients and controls. Results: Compared with the healthy control group, patients group showed significantly decreased fALFF in left superior temporal gyrus and parietal lobe (MNI coordinate: x=-42, -57; y=-3, -21; z=-12, 9; voxels: 22, 32; t=-4.792 3, -5.269 7; Alphasim corrected, corrected P<0.05); patients group showed significantly increased fALFF in left frontal lobe and medial frontal gyrus, right superior frontal gyrus, Postcentral Gyrus, caudate, (MNI coordinate: x=-42, -21, 12, 27, 15; y=54, 39, 48, -18, 15; z=0, 21, 33, 30, 9; voxels: 12, 21, 17, 28, 18; t=4.784 8, 4.90 7, 4.861 5, 5.444 1, 4.270 4; Alphasim corrected, corrected P<0.05). When included age as a covariant, the analysis found that the brain region with significant fALFF change was the left thalamus with decreased fALFF (MNI coordinate: x=-6, y=-12, z=24; voxels: 9; t=-4.268 4; Alphasim corrected, corrected P<0.05) in patients group, while for other brain regions, there was no obvious change in the fALFF, compared with healthy group. Conclusion: Compared with control group, the results indicate that there are intrinsic brain activity abnormalities of some brain regions in childhood and adolescence-onset schizophrenia.
PMID: 27903342 [PubMed - in process]