Specific somatotopic organization of functional connections of the primary motor network during resting state

Translational In Vivo Assays in Behavioral Biology

The failure of preclinical research to advance successful candidate medications in psychiatry has created a paradigmatic crisis in psychiatry. The Research Domain Criteria (RDoC) initiative was designed to remedy this situation with a neuroscience-based approach that employs multimodal and cross-species in vivo methodology to increase the probability of translational findings and, consequently, drug discovery. The present review underscores the feasibility of this methodological approach by briefly reviewing, first, the use of multidimensional and cross-species methodologies in traditional behavioral pharmacology and, subsequently, the utility of this approach in contemporary neuroimaging and electrophysiology research-with a focus on the value of functionally homologous studies in nonhuman and human subjects. The final section provides a brief review of the RDoC, with a focus on the potential strengths and weaknesses of its domain-based underpinnings. Optimistically, this mechanistic and multidimensional approach in neuropsychiatric research will lead to novel therapeutics for the management of neuropsychiatric disorders.

Somatotopic disruption of the functional connectivity of the primary sensorimotor cortex in complex regional pain syndrome type 1

In complex regional pain syndrome (CRPS), the representation area of the affected limb in the primary sensorimotor cortex (SM1) reacts abnormally during sensory stimulation and motor actions. We recorded 3T functional magnetic resonance imaging resting-state data from 17 upper-limb CRPS type 1 patients and 19 healthy control subjects to identify alterations of patients' SM1 function during spontaneous pain and to find out how the spatial distribution of these alterations were related to peripheral symptoms. Seed-based correlations and independent component analyses indicated that patients' upper-limb SM1 representation areas display (i) reduced interhemispheric connectivity, associated with the combined effect of intensity and spatial extent of limb pain, (ii) increased connectivity with the right anterior insula that positively correlated with the duration of CRPS, (iii) increased connectivity with periaqueductal gray matter, and (iv) disengagement from the other parts of the SM1 network. These findings, now reported for the first time in CRPS, parallel the alterations found in patients suffering from other chronic pain conditions or from limb denervation; they also agree with findings in healthy persons who are exposed to experimental pain or have used their limbs asymmetrically. Our results suggest that CRPS is associated with a sustained and somatotopically specific alteration of SM1 function, that has correspondence to the spatial distribution of the peripheral manifestations and to the duration of the syndrome.

Altered functional connectivity of the primary motor cortex in tremor dominant and postural instability gait difficulty subtypes of early drug-naive Parkinson's disease patients

Background

The primary motor cortex (M1) is an important hub in the motor circuitry of Parkinson's disease (PD), but the subregions' function and their correlation to tremor dominant (TD) and postural instability and gait disturbance (PIGD) with PD remain unclear. This study aimed to determine whether the functional connectivity (FC) of the M1 subregions varied between the PD and PIGD subtypes.

Methods

We recruited 28 TD patients, 49 PIGD patients, and 42 healthy controls (HCs). M1 was divided into 12 regions of interest using the Human Brainnetome Atlas template to compare FC among these groups.

Results

Compared with HCs, TD and PIGD patients exhibited increased FC between the left upper limb region (A4UL_L) and the right caudate nucleus (CAU)/left putamen (PUT), between the right A4UL (A4UL_R) and the left anterior cingulate and paracingulate gyri (ACG)/bilateral cerebellum4_5 (CRBL4_5)/left PUT/right CAU/left supramarginal gyrus/left middle frontal gyrus (MFG), as well as decreased connectivity between the A4UL_L and the left postcentral gyrus and the bilateral cuneus, and between the A4UL_R and the right inferior occipital gyrus. TD patients showed increased FC between the right caudal dorsolateral area 6 (A6CDL_R) and the left ACG/right MFG, between the A4UL_L and the right CRBL6/right middle frontal gyrus, orbital part/bilateral inferior frontal gyrus, and orbital part (ORBinf), and between the A4UL_R and the left ORBinf/right MFG/right insula (INS). PIGD patients displayed increased connectivity between the A4UL_L and the left CRBL4_5. Compared with PIGD patients, TD patients exhibited increased connectivity between the A6CDL_R and the left ACG/right MFG and between the A4UL_R and the left ACG/left ORBinf/right INS/right MFG. Furthermore, in TD and PIGD groups, the FC strength between the A6CDL_R and right MFG was negatively correlated with PIGD scores, while the FC strength between the A4UL_R and left ORBinf/right INS was positively correlated with TD scores and tremor scores.

Conclusion

Our results demonstrated that early TD and PIGD patients share some common injury and compensatory mechanisms. TD patients occupied more resources in the MFG, ORBinf, INS, and ACG, which can be used as biomarkers to distinguish them from PIGD patients.

A dual larynx motor networks hypothesis

Humans are vocal modulators par excellence. This ability is supported in part by the dual representation of the laryngeal muscles in the motor cortex. Movement, however, is not the product of motor cortex alone but of a broader motor network. This network consists of brain regions that contain somatotopic maps that parallel the organization in motor cortex. We therefore present a novel hypothesis that the dual laryngeal representation is repeated throughout the broader motor network. In support of the hypothesis, we review existing literature that demonstrates the existence of network-wide somatotopy and present initial evidence for the hypothesis' plausibility. Understanding how this uniquely human phenotype in motor cortex interacts with broader brain networks is an important step toward understanding how humans evolved the ability to speak. We further suggest that this system may provide a means to study how individual components of the nervous system evolved within the context of neuronal networks. This article is part of the theme issue 'Voice modulation: from origin and mechanism to social impact (Part I)'.

Paradoxical facilitation alongside interhemispheric inhibition

Neurophysiological experiments using transcranial magnetic stimulation (TMS) have sought to probe the function of the motor division of the corpus callosum. Primary motor cortex sends projections via the corpus callosum with a net inhibitory influence on the homologous region of the opposite hemisphere. Interhemispheric inhibition (IHI) experiments probe this inhibitory pathway. A test stimulus (TS) delivered to the motor cortex in one hemisphere elicits motor evoked potentials (MEPs) in a target muscle, while a conditioning stimulus (CS) applied to the homologous region of the opposite hemisphere modulates the effect of the TS. We predicted that large CS MEPs would be associated with increased IHI since they should be a reliable index of how effectively contralateral motor cortex was stimulated and therefore of the magnitude of interhemispheric inhibition. However, we observed a strong tendency for larger CS MEPs to be associated with reduced interhemispheric inhibition which in the extreme lead to a net effect of facilitation. This surprising effect was large, systematic, and observed in nearly all participants. We outline several hypotheses for mechanisms which may underlie this phenomenon to guide future research.

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

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We introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data.

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sPROFUMO hierarchically estimates fMRI networks for the population and every individual.

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We characterised high dimensional resting state fMRI networks from UK Biobank.

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Model outperforms ICA and dual regression for estimation of individual-specific network topography.

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We demonstrate the model's utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity.

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

Distinct Functional and Structural Connectivity of the Human Hand-Knob Supported by Intraoperative Findings

Fine motor skills rely on the control of hand muscles exerted by a region of primary motor cortex (M1) that has been extensively investigated in monkeys. Although neuroimaging enables the exploration of this system also in humans, indirect measurements of brain activity prevent causal definitions of hand motor representations, which can be achieved using data obtained during brain mapping in tumor patients. High-frequency direct electrical stimulation delivered at rest (HF-DES-Rest) on the hand-knob region of the precentral gyrus has identified two sectors showing differences in cortical excitability. Using quantitative analysis of motor output elicited with HF DES-Rest, we characterized two sectors based on their excitability, higher in the posterior and lower in the anterior sector. We studied whether the different cortical excitability of these two regions reflected differences in functional connectivity (FC) and structural connectivity (SC). Using healthy adults from the Human Connectome Project (HCP), we computed FC and SC of the anterior and the posterior hand-knob sectors identified within a large cohort of patients. The comparison of FC of the two seeds showed that the anterior hand-knob, relative to the posterior hand-knob, showed stronger functional connections with a bilateral set of parietofrontal areas responsible for integrating perceptual and cognitive hand-related sensorimotor processes necessary for goal-related actions. This was reflected in different patterns of SC between the two sectors. Our results suggest that the human hand-knob is a functionally and structurally heterogeneous region organized along a motor-cognitive gradient.SIGNIFICANCE STATEMENT The capability to perform complex manipulative tasks is one of the major characteristics of primates and relies on the fine control of hand muscles exerted by a highly specialized region of the precentral gyrus, often termed the "hand-knob" sector. Using intraoperative brain mapping, we identify two hand-knob sectors (posterior and anterior) characterized by differences in cortical excitability. Based on resting-state functional connectivity (FC) and tractography in healthy subjects, we show that posterior and anterior hand-knob sectors differ in their functional connectivity (FC) and structural connectivity (SC) with frontoparietal regions. Thus, anteroposterior differences in cortical excitability are paralleled by differences in FC and SC that likely reflect a motor (posterior) to cognitive (anterior) organization of this cortical region.

Effective connectivity differences in motor network during passive movement of paretic and non-paretic ankles in subacute stroke patients

Background

A better understanding of the neural changes associated with paresis in stroke patients could have important implications for therapeutic approaches. Dynamic Causal Modeling (DCM) for functional magnetic resonance imaging (fMRI) is commonly used for analyzing effective connectivity patterns of brain networks due to its significant property of modeling neural states behind fMRI signals. We applied this technique to analyze the differences between motor networks (MNW) activated by continuous passive movement (CPM) of paretic and non-paretic ankles in subacute stroke patients. This study aimed to identify CPM induced connectivity characteristics of the primary sensory area (S1) and the differences in extrinsic directed connections of the MNW and to explain the hemodynamic differences of brain regions of MNW.

Methods

For the network analysis, we used ten stroke patients’ task fMRI data collected under CPMs of both ankles. Regions for the MNW, the primary motor cortex (M1), the premotor cortex (PM), the supplementary motor area (SMA) and the S1 were defined in a data-driven way, by independent component analysis. For the network analysis of both CPMs, we compared twelve models organized into two model-families, depending on the S1 connections and input stimulus modeling. Using DCM, we evaluated the extrinsic connectivity strengths and hemodynamic parameters of both stimulations of all patients.

Results

After a statistical comparison of the extrinsic connections and their modulations of the “best model”, we concluded that three contralateral self-inhibitions (cM1, cS1 and cSMA), one contralateral inter-regional connection (cSMA→cM1), and one interhemispheric connection (cM1→iM1) were significantly different. Our research shows that hemodynamic parameters can be estimated with the Balloon model using DCM but the parameters do not change with stroke.

Conclusions

Our results confirm that the DCM-based connectivity analyses combined with Bayesian model selection may be a useful technique for quantifying the alteration or differences in the characteristics of the motor network in subacute stage stroke patients and in determining the degree of MNW changes.

Planning face, hand, and leg movements: anatomical constraints on preparatory inhibition

Motor-evoked potentials (MEPs), elicited by transcranial magnetic stimulation (TMS) over the motor cortex, are reduced during the preparatory period in delayed response tasks. In this study we examined how MEP suppression varies as a function of the anatomical organization of the motor cortex. MEPs were recorded from a left index muscle while participants prepared a hand or leg movement in experiment 1 or prepared an eye or mouth movement in experiment 2. In this manner, we assessed if the level of MEP suppression in a hand muscle varied as a function of the anatomical distance between the agonist for the forthcoming movement and the muscle targeted by TMS. MEP suppression was attenuated when the cued effector was anatomically distant from the hand (e.g., leg or facial movement compared with finger movement). A similar effect was observed in experiment 3 in which MEPs were recorded from a muscle in the leg and the forthcoming movement involved the upper limb or face. These results demonstrate an important constraint on preparatory inhibition: it is sufficiently broad to be manifest in a muscle that is not involved in the task, but it is not global, showing a marked attenuation when the agonist muscle belongs to a different segment of the body. NEW & NOTEWORTHY Using transcranial magnetic stimulation, we examined changes in corticospinal excitability as people prepared to move. Consistent with previous work, we observed a reduction in excitability during the preparatory period, an effect observed in both task-relevant and task-irrelevant muscles. However, this preparatory inhibition is anatomically constrained, attenuated in muscles belonging to a different body segment than the agonist of the forthcoming movement.

Quasi-periodic patterns contribute to functional connectivity in the brain

Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood. It has been postulated that low-frequency neural activity plays a key role in establishing the functional architecture of the brain. Quasi-periodic patterns (QPPs) are a reliably observable form of low-frequency neural activity that involve the default mode and task positive networks. Here, QPPs from resting-state and working memory task-performing individuals were acquired. The spatiotemporal pattern, strength, and frequency of the QPPs between the two groups were compared and the contribution of QPPs to functional connectivity in the brain was measured. In task-performing individuals, the spatiotemporal pattern of the QPP changes, particularly in task-relevant regions, and the QPP tends to occur with greater strength and frequency. Differences in the QPPs between the two groups could partially account for the variance in functional connectivity between resting-state and task-performing individuals. The QPPs contribute strongly to connectivity in the default mode and task positive networks and to the strength of anti-correlation seen between the two networks. Many of the connections affected by QPPs are also disrupted during several neurological disorders. These findings contribute to understanding the dynamic neural processes that give rise to functional connectivity in the brain and how they may be disrupted during disease.

Differential patterns of age-related cortical and subcortical functional connectivity in 6-to-10 year old children: A connectome-wide association study

INTRODUCTION

Typical brain development is characterized by specific patterns of maturation of functional networks. Cortico-cortical connectivity generally increases, whereas subcortico-cortical connections often decrease. Little is known about connectivity changes amongst different subcortical regions in typical development.

METHODS

This study examined age- and gender-related differences in functional connectivity between and within cortical and subcortical regions using two different approaches. The participants included 411 six- to ten-year-old typically developing children sampled from the population-based Generation R study. Functional connectomes were defined in native space using regions of interest from subject-specific FreeSurfer segmentations. Connections were defined as: (a) the correlation between regional mean time-series; and (b) the focal maximum of voxel-wise correlations within FreeSurfer regions. The association of age and gender with each functional connection was determined using linear regression. The preprocessing included the exclusion of children with excessive head motion and scrubbing to reduce the influence of minor head motion during scanning.

RESULTS

Cortico-cortical associations echoed previous findings that connectivity shifts from short to long-range with age. Subcortico-cortical associations with age were primarily negative in the focal network approach but were both positive and negative in the mean time-series network approach. Between subcortical regions, age-related associations were negative in both network approaches. Few connections had significant associations with gender.

CONCLUSIONS

The present study replicates previously reported age-related patterns of connectivity in a relatively narrow age-range of children. In addition, we extended these findings by demonstrating decreased connectivity within the subcortex with increasing age. Lastly, we show the utility of a more focal approach that challenges the spatial assumptions made by the traditional mean time series approach.

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 connectivity corresponding to the tonotopic differentiation of the human auditory cortex

Recent research has demonstrated that resting-state functional connectivity (RS-FC) within the human auditory cortex (HAC) is frequency-selective, but whether RS-FC between the HAC and other brain areas is differentiated by frequency remains unclear. Three types of data were collected in this study, including resting-state functional magnetic resonance imaging (fMRI) data, task-based fMRI data using six pure tone stimuli (200, 400, 800, 1,600, 3,200, and 6,400 Hz), and structural imaging data. We first used task-based fMRI to identify frequency-selective cortical regions in the HAC. Six regions of interest (ROIs) were defined based on the responses of 50 participants to the six pure tone stimuli. Then, these ROIs were used as seeds to determine RS-FC between the HAC and other brain regions. The results showed that there was RS-FC between the HAC and brain regions that included the superior temporal gyrus, dorsolateral prefrontal cortex (DL-PFC), parietal cortex, occipital lobe, and subcortical structures. Importantly, significant differences in FC were observed among most of the brain regions that showed RS-FC with the HAC. Specifically, there was stronger RS-FC between (1) low-frequency (200 and 400 Hz) regions and brain regions including the premotor cortex, somatosensory/-association cortex, and DL-PFC; (2) intermediate-frequency (800 and 1,600 Hz) regions and brain regions including the anterior/posterior superior temporal sulcus, supramarginal gyrus, and inferior frontal cortex; (3) intermediate/low-frequency regions and vision-related regions; (4) high-frequency (3,200 and 6,400 Hz) regions and the anterior cingulate cortex or left DL-PFC. These findings demonstrate that RS-FC between the HAC and other brain areas is frequency selective.

Kernel-Regularized ICA for Computing Functional Topography from Resting-state fMRI

Topographic regularity is a fundamental property in brain connectivity. In this work, we present a novel method for studying topographic regularity of functional connectivity based on resting-state fMRI (rfMRI), which is widely available and easy to acquire in large-scale studies. The main idea in our method is the incorporation of topographically regular structural connectivity for independent component analysis (ICA). This is enabled by the recent development of novel tractography and tract filtering algorithms that can generate highly organized fiber bundles connecting different brain regions. By leveraging these cutting-edge tractography algorithms, here we develop a kernel-regularized ICA method for the extraction of functional topography with rfMRI signals. In our experiments, we use rfMRI scans of 35 unrelated, right-handed subjects from the Human Connectome Project (HCP) to study the functional topography of the motor cortex. We first demonstrate that our method can generate functional connectivity maps with more regular topography than conventional group ICA. We also show that the components extracted by our algorithm are able to capture co-activation patterns that respect the organized topography of the motor cortex across the hemisphere. Finally, we show that our method achieves improved reproducibility as compared to conventional group ICA.

Strong Functional Connectivity among Homotopic Brain Areas Is Vital for Motor Control in Unilateral Limb Movement

The mechanism underlying brain region organization for motor control in humans remains poorly understood. In this functional magnetic resonance imaging (fMRI) study, right-handed volunteers were tasked to maintain unilateral foot movements on the right and left sides as consistently as possible. We aimed to identify the similarities and differences between brain motor networks of the two conditions. We recruited 18 right-handed healthy volunteers aged 25 ± 2.3 years and used a whole-body 3T system for magnetic resonance (MR) scanning. Image analysis was performed using SPM8, Conn toolbox and Brain Connectivity Toolbox. We determined a craniocaudally distributed, mirror-symmetrical modular structure. The functional connectivity between homotopic brain areas was generally stronger than the intrahemispheric connections, and such strong connectivity led to the abovementioned modular structure. Our findings indicated that the interhemispheric functional interaction between homotopic brain areas is more intensive than the interaction along the conventional top-down and bottom-up pathways within the brain during unilateral limb movement. The detected strong interhemispheric horizontal functional interaction is an important aspect of motor control but often neglected or underestimated. The strong interhemispheric connectivity may explain the physiological phenomena and effects of promising therapeutic approaches. Further accurate and effective therapeutic methods may be developed on the basis of our findings.

Positive and Negative Somatotopic BOLD Responses in Contralateral Versus Ipsilateral Penfield Homunculus

One of the basic properties of sensory cortices is their topographical organization. Most imaging studies explored this organization using the positive blood oxygenation level-dependent (BOLD) signal. Here, we studied the topographical organization of both positive and negative BOLD in contralateral and ipsilateral primary somatosensory cortex (S1). Using phase-locking mapping methods, we verified the topographical organization of contralateral S1, and further showed that different body segments elicit pronounced negative BOLD responses in both hemispheres. In the contralateral hemisphere, we found a sharpening mechanism in which stimulation of a given body segment triggered a gradient of activation with a significant deactivation in more remote areas. In the ipsilateral cortex, deactivation was not only located in the homolog area of the stimulated parts but rather was widespread across many parts of S1. Additionally, analysis of resting-state functional magnetic resonance imaging signal showed a gradient of connectivity to the neighboring contralateral body parts as well as to the ipsilateral homologous area for each body part. Taken together, our results indicate a complex pattern of baseline and activity-dependent responses in the contralateral and ipsilateral sides. Both primary sensory areas were characterized by unique negative BOLD responses, suggesting that they are an important component in topographic organization of sensory cortices.

Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting-state functional connectivity

The cerebral cortex is well known to display a large variation in excitatory and inhibitory chemoarchitecture, but the effect of this variation on global scale functional neural communication and synchronization patterns remains less well understood. Here, we provide evidence of the chemoarchitecture of cortical regions to be associated with large-scale region-to-region resting-state functional connectivity. We assessed the excitatory versus inhibitory chemoarchitecture of cortical areas as an ExIn ratio between receptor density mappings of excitatory (AMPA, M1 ) and inhibitory (GABAA , M2 ) receptors, computed on the basis of data collated from pioneering studies of autoradiography mappings as present in literature of the human (2 datasets) and macaque (1 dataset) cortex. Cortical variation in ExIn ratio significantly correlated with total level of functional connectivity as derived from resting-state functional connectivity recordings of cortical areas across all three datasets (human I: P = 0.0004; human II: P = 0.0008; macaque: P = 0.0007), suggesting cortical areas with an overall more excitatory character to show higher levels of intrinsic functional connectivity during resting-state. Our findings are indicative of the microscale chemoarchitecture of cortical regions to be related to resting-state fMRI connectivity patterns at the global system's level of connectome organization. Hum Brain Mapp 37:3103-3113, 2016. © 2016 Wiley Periodicals, Inc.

Topological organization of connectivity strength in the rat connectome

The mammalian brain is a complex network of anatomically interconnected regions. Animal studies allow for an invasive measurement of the connections of these networks at the macroscale level by means of neuronal tracing of axonal projections, providing a unique opportunity for the formation of detailed 'connectome maps'. Here we analyzed the macroscale connectome of the rat brain, including detailed information on the macroscale interregional pathways between 67 cortical and subcortical regions as provided by the high-quality, open-access BAMS-II database on rat brain anatomical projections, focusing in particular on the non-uniform distribution of projection strength across pathways. First, network analysis confirmed a small-world, modular and rich club organization of the rat connectome; findings in clear support of previous studies on connectome organization in other mammalian species. More importantly, analyzing network properties of different connection weight classes, we extend previous observations by showing that pathways with different topological roles have significantly different levels of connectivity strength. Among other findings, intramodular connections are shown to display a higher connectivity strength than intermodular connections and hub-to-hub rich club connections are shown to include significantly stronger pathways than connections spanning between peripheral nodes. Furthermore, we show evidence indicating that edges of different weight classes display different topological structures, potentially suggesting varying roles and origins of pathways in the mammalian brain network.

Spontaneous Neural Dynamics and Multi-scale Network Organization

Spontaneous neural activity has historically been viewed as task-irrelevant noise that should be controlled for via experimental design, and removed through data analysis. However, electrophysiology and functional MRI studies of spontaneous activity patterns, which have greatly increased in number over the past decade, have revealed a close correspondence between these intrinsic patterns and the structural network architecture of functional brain circuits. In particular, by analyzing the large-scale covariation of spontaneous hemodynamics, researchers are able to reliably identify functional networks in the human brain. Subsequent work has sought to identify the corresponding neural signatures via electrophysiological measurements, as this would elucidate the neural origin of spontaneous hemodynamics and would reveal the temporal dynamics of these processes across slower and faster timescales. Here we survey common approaches to quantifying spontaneous neural activity, reviewing their empirical success, and their correspondence with the findings of neuroimaging. We emphasize invasive electrophysiological measurements, which are amenable to amplitude- and phase-based analyses, and which can report variations in connectivity with high spatiotemporal precision. After summarizing key findings from the human brain, we survey work in animal models that display similar multi-scale properties. We highlight that, across many spatiotemporal scales, the covariance structure of spontaneous neural activity reflects structural properties of neural networks and dynamically tracks their functional repertoire.

Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia

Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, “bi-modal comparison plots” show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using “worm plots”. Group differences in connections are examined with an existing visualization, the “connectogram”. These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the “Statistical Analysis of Minimum cost path based Structural Connectivity” method and the average fractional anisotropy along the fiber. The functional measures were Pearson’s correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.

Resting state signatures of domain and demand-specific working memory performance

Working memory (WM) is one of the key constructs in understanding higher-level cognition. We examined whether patterns of activity in the resting state of individual subjects are correlated with their off-line working and short-term memory capabilities. Participants completed a resting-state fMRI scan and off-line working and short-term memory (STM) tests with both verbal and visual materials. We calculated fractional amplitude of low frequency fluctuations (fALFF) from the resting state data, and also computed connectivity between seeds placed in frontal and parietal lobes. Correlating fALFF values with behavioral measures showed that the fALFF values in a widespread fronto-parietal network during rest were positively correlated with a combined memory measure. In addition, STM showed a significant correlation with fALFF within the right angular gyrus and left middle occipital gyrus, whereas WM was correlated with fALFF values within the right IPS and left dorsomedial cerebellar cortex. Furthermore, verbal and visuospatial memory capacities were associated with dissociable patterns of low-frequency fluctuations. Seed-based connectivity showed correlations with the verbal WM measure in the left hemisphere, and with the visual WM measure in the right hemisphere. These findings contribute to our understanding of how differences in spontaneous low-frequency fluctuations at rest are correlated with differences in cognitive performance.

New whole-body sensory-motor gradients revealed using phase-locked analysis and verified using multivoxel pattern analysis and functional connectivity

Topographic organization is one of the main principles of organization in the human brain. Specifically, whole-brain topographic mapping using spectral analysis is responsible for one of the greatest advances in vision research. Thus, it is intriguing that although topography is a key feature also in the motor system, whole-body somatosensory-motor mapping using spectral analysis has not been conducted in humans outside M1/SMA. Here, using this method, we were able to map a homunculus in the globus pallidus, a key target area for deep brain stimulation, which has not been mapped noninvasively or in healthy subjects. The analysis clarifies contradictory and partial results regarding somatotopy in the caudal-cingulate zone and rostral-cingulate zone in the medial wall and in the putamen. Most of the results were confirmed at the single-subject level and were found to be compatible with results from animal studies. Using multivoxel pattern analysis, we could predict movements of individual body parts in these homunculi, thus confirming that they contain somatotopic information. Using functional connectivity, we demonstrate interhemispheric functional somatotopic connectivity of these homunculi, such that the somatotopy in one hemisphere could have been found given the connectivity pattern of the corresponding regions of interest in the other hemisphere. When inspecting the somatotopic and nonsomatotopic connectivity patterns, a similarity index indicated that the pattern of connected and nonconnected regions of interest across different homunculi is similar for different body parts and hemispheres. The results show that topographical gradients are even more widespread than previously assumed in the somatosensory-motor system. Spectral analysis can thus potentially serve as a gold standard for defining somatosensory-motor system areas for basic research and clinical applications.

Age-related changes in resting-state networks of a large sample size of healthy elderly

AIMS

Population aging is burdening the society globally, and the evaluation of functional networks is the key toward understanding cognitive changes in normal aging. However, the effect of age on default mode subnetworks has not been documented well, and age-related changes in many resting-state networks remain debatable. The purpose of this study was to propose more precise results for these issues using a large sample size.

METHODS

We used group-level meta-ICA analysis and dual regression approach for identifying resting-state networks from functional magnetic resonance imaging data of 430 healthy elderly participants. Partial correlation was used to observe age-related correlations within and between resting-state networks.

RESULTS

In the default mode network, only the ventral subnetwork negatively correlated with age. Age-related decrease in functional connectivity was also noted in the auditory, right frontoparietal, sensorimotor, and visual medial networks. Further, some age-related increases and decreases were observed for between-network correlations.

CONCLUSION

The results of this study suggest that only the ventral default mode subnetwork had age-related decline in functional connectivity and several reverse patterns of resting-state networks for network development. Understanding age-related network changes may provide solutions for the impact of population aging and diagnosis of neurodegenerative diseases.

Functional reorganization of the default mode network across chronic pain conditions

Chronic pain is associated with neuronal plasticity. Here we use resting-state functional magnetic resonance imaging to investigate functional changes in patients suffering from chronic back pain (CBP), complex regional pain syndrome (CRPS) and knee osteoarthritis (OA). We isolated five meaningful resting-state networks across the groups, of which only the default mode network (DMN) exhibited deviations from healthy controls. All patient groups showed decreased connectivity of medial prefrontal cortex (MPFC) to the posterior constituents of the DMN, and increased connectivity to the insular cortex in proportion to the intensity of pain. Multiple DMN regions, especially the MPFC, exhibited increased high frequency oscillations, conjoined with decreased phase locking with parietal regions involved in processing attention. Both phase and frequency changes correlated to pain duration in OA and CBP patients. Thus chronic pain seems to reorganize the dynamics of the DMN and as such reflect the maladaptive physiology of different types of chronic pain.

Frequency Selectivity of Voxel-by-Voxel Functional Connectivity in Human Auditory Cortex

While functional connectivity in the human cortex has been increasingly studied, its relationship to cortical representation of sensory features has not been documented as much. We used functional magnetic resonance imaging to demonstrate that voxel-by-voxel intrinsic functional connectivity (FC) is selective to frequency preference of voxels in the human auditory cortex. Thus, FC was significantly higher for voxels with similar frequency tuning than for voxels with dissimilar tuning functions. Frequency-selective FC, measured via the correlation of residual hemodynamic activity, was not explained by generic FC that is dependent on spatial distance over the cortex. This pattern remained even when FC was computed using residual activity taken from resting epochs. Further analysis showed that voxels in the core fields in the right hemisphere have a higher frequency selectivity in within-area FC than their counterpart in the left hemisphere, or than in the noncore-fields in the same hemisphere. Frequency-selective FC is consistent with previous findings of topographically organized FC in the human visual and motor cortices. The high degree of frequency selectivity in the right core area is in line with findings and theoretical proposals regarding the asymmetry of human auditory cortex for spectral processing.

Disruption of functional organization within the primary motor cortex in children with autism

Accumulating evidence suggests that motor impairments are prevalent in autism spectrum disorder (ASD), relate to the social and communicative deficits at the core of the diagnosis and may reflect abnormal connectivity within brain networks underlying motor control and learning. Parcellation of resting-state functional connectivity data using spectral clustering approaches has been shown to be an effective means of visualizing functional organization within the brain but has most commonly been applied to explorations of normal brain function. This article presents a parcellation of a key area of the motor network, the primary motor cortex (M1), a key area of the motor control network, in adults, typically developing (TD) children and children with ASD and introduces methods for selecting the number of parcels, matching parcels across groups and testing group differences. The parcellation is based solely on patterns of connectivity between individual M1 voxels and all voxels outside of M1, and within all groups, a gross dorsomedial to ventrolateral organization emerged within M1 which was left-right symmetric. Although this gross organizational scheme was present in both groups of children, statistically significant group differences in the size and segregation of M1 parcels within regions of the motor homunculus corresponding to the upper and lower limbs were observed. Qualitative comparison of the M1 parcellation for children with ASD with that of younger and older TD children suggests that these organizational differences, with a lack of differentiation between lower limb/trunk regions and upper limb/hand regions, may be due, at least in part, to a delay in functional specialization within the motor cortex.

The Laplacian spectrum of neural networks

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these "conventional" graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks.

Discriminative analysis of non-linear brain connectivity in schizophrenia: an fMRI Study

Background: Dysfunctional integration of distributed brain networks is believed to be the cause of schizophrenia, and resting-state functional connectivity analyses of schizophrenia have attracted considerable attention in recent years. Unfortunately, existing functional connectivity analyses of schizophrenia have been mostly limited to linear associations.

Objective: The objective of the present study is to evaluate the discriminative power of non-linear functional connectivity and identify its changes in schizophrenia.

Method: A novel measure utilizing the extended maximal information coefficient was introduced to construct non-linear functional connectivity. In conjunction with multivariate pattern analysis, the new functional connectivity successfully discriminated schizophrenic patients from healthy controls with relative higher accuracy rate than the linear measure.

Result: We found that the strength of the identified non-linear functional connections involved in the classification increased in patients with schizophrenia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the non-linear and linear connectivity have similar but not completely the same spatial distribution in human brain.

Conclusion: The classification results suggest that the non-linear functional connectivity provided useful discriminative power in diagnosis of schizophrenia, and the inverse but similar spatial distributed changes between the non-linear and linear measure may indicate the underlying compensatory mechanism and the complex neuronal synchronization underlying the symptom of schizophrenia.

Generic inhibition of the selected movement and constrained inhibition of nonselected movements during response preparation

Previous studies have identified two inhibitory mechanisms that operate during action selection and preparation. One mechanism, competition resolution, is manifest in the inhibition of the nonselected response and attributed to competition between candidate actions. The second mechanism, impulse control, is manifest in the inhibition of the selected response and is presumably invoked to prevent premature response. To identify constraints on the operation of these two inhibitory mechanisms, we manipulated the effectors used for the response alternatives, measuring changes in corticospinal excitability with motor-evoked potentials to TMS. Inhibition of the selected response (impulse control) was independent of the task context, consistent with a model in which this form of inhibition is automatically triggered as part of response preparation. In contrast, inhibition of the nonselected response (competition resolution) was context-dependent. Inhibition of the nonselected response was observed when the response alternatives involved movements of the upper limbs but was absent when one response alternative involved an upper limb and the other involved a lower limb. Interestingly, competition resolution for pairs of upper limbs did not require homologous effectors, observed when a left index finger response was pitted with either a nonhomologous right index finger movement or a right arm movement. These results argue against models in which competition resolution is viewed as a generic or fully flexible process, as well as models based on strong anatomical constraints. Rather, they are consistent with models in which inhibition for action selection is constrained by the similarity between the potential responses, perhaps reflecting an experience-dependent mechanism sensitive to the past history of competitive interactions.

Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients

Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer's Disease (AD). However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI) method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN) group. Our main finding is that the largest homotopic module (defined as the insula module) in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole) of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC) was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A) and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups). Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.

The change of functional connectivity specificity in rats under various anesthesia levels and its neural origin

Spatiotemporal correlations of spontaneous blood oxygenation level dependent (BOLD) signals measured in the resting brain have been found to imply many resting-state coherent networks under both awake/conscious and anesthetized/unconscious conditions. To understand the resting-state brain networks in the unconscious state, spontaneous BOLD signals from the rat sensorimotor cortex were studied across a wide range of anesthesia levels induced by isoflurane. Distinct resting-state networks covering functionally specific sub-regions of the sensorimotor system were observed under light anesthesia with 1.0% isoflurane; however, they gradually merged into a highly synchronized and spatially less-specific network under deep anesthesia with 1.8% isoflurane. The EEG power correlations recorded using three electrodes from a separate group of rats showed similar dependency on anesthesia depth, suggesting the neural origin of the change in functional connectivity specificity. The specific-to-less-specific transition of resting-state networks may reflect a functional reorganization of the brain at different anesthesia levels or brain states.

Resting-state fMRI in the Human Connectome Project

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2×2×2 mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

Graph network analysis of immediate motor-learning induced changes in resting state BOLD

Recent studies have demonstrated that following learning tasks, changes in the resting state activity of the brain shape regional connections in functionally specific circuits. Here we expand on these findings by comparing changes induced in the resting state immediately following four motor tasks. Two groups of participants performed a visuo-motor joystick task with one group adapting to a transformed relationship between joystick and cursor. Two other groups were trained in either explicit or implicit procedural sequence learning. Resting state BOLD data were collected immediately before and after the tasks. We then used graph theory-based approaches that include statistical measures of functional integration and segregation to characterize changes in biologically plausible brain connectivity networks within each group. Our results demonstrate that motor learning reorganizes resting brain networks with an increase in local information transfer, as indicated by local efficiency measures that affect the brain's small world network architecture. This was particularly apparent when comparing two distinct forms of explicit motor learning: procedural learning and the joystick learning task. Both groups showed notable increases in local efficiency. However, a change in local efficiency in the inferior frontal and cerebellar regions also distinguishes between the two learning tasks. Additional graph analytic measures on the "non-learning" visuo-motor performance task revealed reversed topological patterns in comparison with the three learning tasks. These findings underscore the utility of graph-based network analysis as a novel means to compare both regional and global changes in functional brain connectivity in the resting state following motor learning tasks.

Quantifying agreement between anatomical and functional interhemispheric correspondences in the resting brain

The human brain is composed of two broadly symmetric cerebral hemispheres, with an abundance of reciprocal anatomical connections between homotopic locations. However, to date, studies of hemispheric symmetries have not identified correspondency precisely due to variable cortical folding patterns. Here we present a method to establish accurate correspondency using position on the unfolded cortical surface relative to gyral and sulcal landmarks. The landmark method is shown to outperform the method of reversing standard volume coordinates, and it is used to quantify the functional symmetry in resting fMRI data throughout the cortex. Resting brain activity was found to be maximally correlated with locations less than 1 cm away on the cortical surface from the corresponding anatomical location in nearly half of the cortex. While select locations exhibited asymmetric patterns, precise symmetric relationships were found to be the norm, with fine-grained symmetric functional maps demonstrated in motor, occipital, and inferior frontal cortex.

Task and task-free FMRI reproducibility comparison for motor network identification

Test-retest reliability of individual functional magnetic resonance imaging (fMRI) results is of importance in clinical practice and longitudinal experiments. While several studies have investigated reliability of task-induced motor network activation, less is known about the reliability of the task-free motor network. Here, we investigate the reproducibility of task-free fMRI, and compare it to motor task activity. Sixteen healthy subjects participated in this study with a test-retest interval of seven weeks. The task-free motor network was assessed with a univariate, seed-voxel-based correlation analysis. Reproducibility was tested by means of intraclass correlation (ICC) values and ratio of overlap. Higher ICC values and a better overlap were found for task fMRI as compared to task-free fMRI. Furthermore, ratio of overlap improved for task fMRI at higher thresholds, while it decreased for task-free fMRI, suggesting a less focal spatial pattern of the motor network during resting state. However, for both techniques the most active voxels were located in the primary motor cortex. This indicates that, just like task fMRI, task-free fMRI can properly identify critical brain areas for motor task performance. Although both fMRI techniques are able to detect the motor network, resting-state fMRI is less reliable than task fMRI.

A correlation-matrix-based hierarchical clustering method for functional connectivity analysis

In this study, a correlation matrix based hierarchical clustering (CMBHC) method is introduced to extract multiple correlation patterns from resting-state functional magnetic resonance imaging (fMRI) data. It was applied to spontaneous fMRI signals acquired from anesthetized rats, and the results were then compared with those obtained using independent component analysis (ICA), one of the most popular multivariate analysis method for analyzing spontaneous fMRI signals. It was demonstrated that the CMBHC has a higher sensitivity than the ICA, particularly on a single run data, for identifying correlation structures with relatively weak connections, for instance, the thalamocortical connections. Compared to the seed-based correlation analysis, the CMBHC does not require a priori information and thus can avoid potential biases caused by seed selection, and multiple patterns can be extracted at one time. In contrast to other multivariate methods, the CMBHC is based on spatiotemporal correlations of fMRI signals and its analysis outcomes are easy to interpret as the strength of functional connectivity. Moreover, its sensitivity of detecting patterns remains relatively high even for a single dataset. In conclusion, the CMBHC method could be a useful tool for investigating resting-state brain connectivity and function.

Modulation of large-scale brain networks by transcranial direct current stimulation evidenced by resting-state functional MRI

BACKGROUND

Brain areas interact mutually to perform particular complex brain functions such as memory or language. Furthermore, under resting-state conditions several spatial patterns have been identified that resemble functional systems involved in cognitive functions. Among these, the default-mode network (DMN), which is consistently deactivated during task periods and is related to a variety of cognitive functions, has attracted most attention. In addition, in resting-state conditions some brain areas engaged in focused attention (such as the anticorrelated network, AN) show a strong negative correlation with DMN; as task demand increases, AN activity rises, and DMN activity falls.

OBJECTIVE

We combined transcranial direct current stimulation (tDCS) with functional magnetic resonance imaging (fMRI) to investigate these brain network dynamics.

METHODS

Ten healthy young volunteers underwent four blocks of resting-state fMRI (10-minutes), each of them immediately after 20 minutes of sham or active tDCS (2 mA), on two different days. On the first day the anodal electrode was placed over the left dorsolateral prefrontal cortex (DLPFC) (part of the AN) with the cathode over the contralateral supraorbital area, and on the second day, the electrode arrangement was reversed (anode right-DLPFC, cathode left-supraorbital).

RESULTS

After active stimulation, functional network connectivity revealed increased synchrony within the AN components and reduced synchrony in the DMN components.

CONCLUSIONS

Our study reveals a reconfiguration of intrinsic brain activity networks after active tDCS. These effects may help to explain earlier reports of improvements in cognitive functions after anodal-tDCS, where increasing cortical excitability may have facilitated reconfiguration of functional brain networks to address upcoming cognitive demands.

Discovering the somatotopic organization of the motor areas of the medial wall using low-frequency BOLD fluctuations

This study explored the somatotopy of the motor areas of the medial wall of the cerebral hemisphere, in the human brain. In a sample of 16 healthy participants, we drew 9 regions of interest (ROI) over the primary motor area (M1), each corresponding to a well-known somatic representation. Using functional magnetic resonance imaging, we investigated the resting state functional connectivity between each selected ROI and the motor areas of the medial wall. The main finding was the identification of a rostrocaudal gradient of connectivity in which the more we move from cranial to caudal body representation areas in M1, the more the corresponding connected area in the medial wall is shifted rostrocaudally, confirming the somatotopic schema found in the SMA. We also reanalyzed data obtained in a previous experiment, we performed using hand and foot motor tasks; the reanalysis consisted in traditional BOLD and functional connectivity analyses. Finally, we performed a meta-analysis of 28 studies of hand and foot motor tasks, mapping their cerebral representations using the tools provided by the Brainmap database. All data converge in confirming a somatotopic representation of the medial wall motor areas, with hand representation placed more rostrally and ventrally than that of the foot.