Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation

Whether functional magnetic resonance imaging (fMRI) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF), we performed simultaneous electroencephalographic (EEG) and fMRI measurements, and subsequent intracerebral EEG (iEEG) recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum). fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging.     

Undirected graphs of frequency-dependent functional connectivity in whole brain networks

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information phi, an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.     

The Hierarchical Brain Network for Face Recognition

Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.     

The chronoarchitecture of the cerebral cortex

We review here a new approach to mapping the human cerebral cortex into distinct subdivisions. Unlike cytoarchitecture or traditional functional imaging, it does not rely on specific anatomical markers or functional hypotheses. Instead, we propose that the unique activity time course (ATC) of each cortical subdivision, elicited during natural conditions, acts as a temporal fingerprint that can be used to segregate cortical subdivisions, map their spatial extent, and reveal their functional and potentially anatomical connectivity. We argue that since the modular organisation of the brain and its connectivity evolved and developed in natural conditions, these are optimal for revealing its organisation. We review the concepts, methodology and first results of this approach, relying on data obtained with functional magnetic resonance imaging (fMRI) when volunteers viewed traditional stimuli or a James Bond movie. Independent component analysis (ICA) was used to identify voxels belonging to distinct functional subdivisions, based on their differential spatio-temporal fingerprints. Many more regions could be segregated during natural viewing, demonstrating that the complexity of natural stimuli leads to more differential responses in more functional modules. We demonstrate that, in a single experiment, a multitude of distinct regions can be identified across the whole brain, even within the visual cortex, including areas V1, V4 and V5. This differentiation is based entirely on the differential ATCs of different areas during natural viewing. Distinct areas can therefore be identified without any a priori hypothesis about their function or spatial location. The areas we identified corresponded anatomically across subjects, and their ATCs showed highly area-specific inter-subject correlations. Furthermore, natural conditions led to a significant de-correlation of interregional ATCs compared to rest, indicating an increase in regional specificity during natural conditions. In contrast, the correlation between ATCs of distant regions of known substantial anatomical connections increased and reflected their known anatomical connectivity pattern. We demonstrate this using the example of the language network involving Broca's and Wernicke's area and homologous areas in the two hemispheres. In conclusion, this new approach to brain mapping may not only serve to identify novel functional subdivisions, but to reveal their connectivity as well.     

Temporal Lobe Cortical Thickness Correlations Differentiate the Migraine Brain from the Healthy Brain

BackgroundInterregional cortical thickness correlations reflect underlying brain structural connectivity and functional connectivity. A few prior studies have shown that migraine is associated with atypical cortical brain structure and atypical functional connectivity amongst cortical regions that participate in sensory processing. However, the specific brain regions that most accurately differentiate the migraine brain from the healthy brain have yet to be determined. The aim of this study was to identify the brain regions that comprised interregional cortical thickness correlations that most differed between migraineurs and healthy controls.MethodsThis was a cross-sectional brain magnetic resonance imaging (MRI) investigation of 64 adults with migraine and 39 healthy control subjects recruited from tertiary-care medical centers and their surrounding communities. All subjects underwent structural brain MRI imaging on a 3T scanner. Cortical thickness was determined for 70 brain regions that cover the cerebral cortex and cortical thickness correlations amongst these regions were calculated. Cortical thickness correlations that best differentiated groups of six migraineurs from controls and vice versa were identified.ResultsA model containing 15 interregional cortical thickness correlations differentiated groups of migraineurs from healthy controls with high accuracy. The right temporal pole was involved in 13 of the 15 interregional correlations while the right middle temporal cortex was involved in the other two.ConclusionsA model consisting of 15 interregional cortical thickness correlations accurately differentiates the brains of small groups of migraineurs from those of healthy controls. Correlations with the right temporal pole were highly represented in this classifier, suggesting that this region plays an important role in migraine pathophysiology.     

Functional Connectivity in the Brain Estimated by Analysis of Gamma Events

It is known that gamma activity is generated by local networks. In this paper we introduced a new approach for estimation of functional connectivity between neuronal networks by measuring temporal relations between peaks of gamma event amplitudes. We have shown in freely moving rats that gamma events recorded between electrodes 1.5 mm apart in the majority of cases, are generated by different neuronal modules interfering with each other. The map of functional connectivity between brain areas during the resting state, created based on gamma event temporal relationships is in agreement with anatomical connections and with maps described by fMRI methods during the resting state. The transition from the resting state to exploratory activity is accompanied by decreased functional connectivity between most brain areas. Our data suggest that functional connectivity between interhemispheric areas depends on GABAergic transmission, while intrahemispheric functional connectivity is kainate receptor dependent. This approach presents opportunities for merging electrographic and fMRI data on brain functional connectivity in normal and pathological conditions.     

Altered Resting-State Functional Connectivity of the Frontal-Striatal Reward System in Social Anxiety Disorder

We investigated differences in the intrinsic functional brain organization (functional connectivity) of the human reward system between healthy control participants and patients with social anxiety disorder. Functional connectivity was measured in the resting-state via functional magnetic resonance imaging (fMRI). 53 patients with social anxiety disorder and 33 healthy control participants underwent a 6-minute resting-state fMRI scan. Functional connectivity of the reward system was analyzed by calculating whole-brain temporal correlations with a bilateral nucleus accumbens seed and a ventromedial prefrontal cortex seed. Patients with social anxiety disorder, relative to the control group, had (1) decreased functional connectivity between the nucleus accumbens seed and other regions associated with reward, including ventromedial prefrontal cortex; (2) decreased functional connectivity between the ventromedial prefrontal cortex seed and lateral prefrontal regions, including the anterior and dorsolateral prefrontal cortices; and (3) increased functional connectivity between both the nucleus accumbens seed and the ventromedial prefrontal cortex seed with more posterior brain regions, including anterior cingulate cortex. Social anxiety disorder appears to be associated with widespread differences in the functional connectivity of the reward system, including markedly decreased functional connectivity between reward regions and between reward regions and lateral prefrontal cortices, and markedly increased functional connectivity between reward regions and posterior brain regions.     

Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity

The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.     

基于空间ICA和时间相关方法的人脑视觉皮层V5区的功能连通性研究

利用功能磁共振成像技术,将空间ICA和时间相关方法相结合来研究不同活动状态下人脑视觉皮层V5区的功能连通性。首先利用空间ICA处理组块视觉运动刺激的数据,定位V5区;然后分别计算静息和连续视觉运动刺激两种稳态下V5区与其它脑区低频振荡的时间相关,检测出该区的功能连通网络。实验结果表明,静息时V5区的功能连通网络更广泛,且与已知的解剖连通一致;当被试接受连续视觉运动刺激时,与V5区连通的脑区网络局限在视觉皮层,此时的网络特定于处理视觉运动这一任务。     

Assessing the influence of different ROI selection strategies on functional connectivity analyses of fMRI data acquired during steady-state conditions

In blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), assessing functional connectivity between and within brain networks from datasets acquired during steady-state conditions has become increasingly common. However, in contrast to connectivity analyses based on task-evoked signal changes, selecting the optimal spatial location of the regions of interest (ROIs) whose timecourses will be extracted and used in subsequent analyses is not straightforward. Moreover, it is also unknown how different choices of the precise anatomical locations within given brain regions influence the estimates of functional connectivity under steady-state conditions. The objective of the present study was to assess the variability in estimates of functional connectivity induced by different anatomical choices of ROI locations for a given brain network. We here targeted the default mode network (DMN) sampled during both resting-state and a continuous verbal 2-back working memory task to compare four different methods to extract ROIs in terms of ROI features (spatial overlap, spatial functional heterogeneity), signal features (signal distribution, mean, variance, correlation) as well as strength of functional connectivity as a function of condition. We show that, while different ROI selection methods produced quantitatively different results, all tested ROI selection methods agreed on the final conclusion that functional connectivity within the DMN decreased during the continuous working memory task compared to rest.     

Teaching about Scientific Origins: Taking Account of Creationism

The purpose of this study was to develop a model for measuring experimental design ability based on functional magnetic resonance imaging (fMRI) during biological inquiry. More specifically, the researchers developed an experimental design task that measures experimental design ability. Using the developed experimental design task, they measured both the paper experimental design ability and the fMRI experimental design ability of subjects. Subjects’ paper experimental design ability was measured using the quotient equation of experimental design ability, and their fMRI experimental design ability using the brain connectivity coefficient. According to the fMRI results, differences in design ability existed among subjects in terms of brain connectivity coefficient level during the experimental design task. The experimental design ability brain connectivity coefficient level and quotient for each subject were analysed. Statistically significant correlations between subjects’ connectivity strength level among brain activation regions and quotient value guided the establishment of a measuring model. The model measured experimental design ability and could predict an individual’s experimental design ability quotient using his or her brain connectivity coefficient. Hence, the model developed for this study for measuring experimental design ability based on fMRI may serve as a practical measurement of students’ scientific experimental design ability. Furthermore, this study could serve as a founding theory for measuring models of other scientific processing abilities such as observation, question generation, classification, hypothesis generation and hypothesis evaluation.     

Assessment of brain interactivity in the motor cortex from the concept of functional connectivity and spectral analysis of fMRI data

Functional magnetic resonance imaging (fMRI) was used to assess the contributions of movement preparation and execution of a visuomotor task in a cerebral motor network. The functional connectivity of the voxel time series between brain regions in the frequency space was investigated by performing spectral analysis of fMRI time series. The regional interactivities between the two portions of the supplementary motor area (pre-SMA and SMA-proper) and the primary motor cortex (M1), defined as a seed region, were evaluated. The spectral parameter of coherence was used to describe a correlation structure in the frequency domain between two voxel-based time series and to infer the strength of the functional interaction within our presumed motor network of connections. The results showed meaningful differences of the functional interactions between the two portions of the SMA and the M1 area depending on the task conditions. This approach demonstrated the existence of a functional dissociation between the pre-SMA and SMA-proper subregions. We therefore conclude that spectral analysis is useful for identifying functional interactions of brain regions and might provide a powerful tool to quantify changes in connectivity profiles associated with various components of an experimental task.     

脑网络：从脑结构到脑功能

人脑是自然界中最复杂的系统之一,不同的功能区域相互作用、互相协调,共同构成一个网络来发挥其功能。人脑是一个复杂的网络,具有高效的“小世界”拓扑属性。本文从脑结构到脑功能方面介绍了从不同模态影像学数据构造脑网络的主要进展,并探讨不同的脑疾病患者脑网络拓扑结构是否发生了异常,以及这些异常特征能否用来进行疾病分类,最后对本领域未来的研究做了简单的展望。     

Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging

Auditory cortex pertains to the processing of sound, which is at the basis of speech or music-related processing¹. However, despite considerable recent progress, the functional properties and lateralization of the human auditory cortex are far from being fully understood. Transcranial Magnetic Stimulation (TMS) is a non-invasive technique that can transiently or lastingly modulate cortical excitability via the application of localized magnetic field pulses, and represents a unique method of exploring plasticity and connectivity. It has only recently begun to be applied to understand auditory cortical function ². An important issue in using TMS is that the physiological consequences of the stimulation are difficult to establish. Although many TMS studies make the implicit assumption that the area targeted by the coil is the area affected, this need not be the case, particularly for complex cognitive functions which depend on interactions across many brain regions ³. One solution to this problem is to combine TMS with functional Magnetic resonance imaging (fMRI). The idea here is that fMRI will provide an index of changes in brain activity associated with TMS. Thus, fMRI would give an independent means of assessing which areas are affected by TMS and how they are modulated ⁴. In addition, fMRI allows the assessment of functional connectivity, which represents a measure of the temporal coupling between distant regions. It can thus be useful not only to measure the net activity modulation induced by TMS in given locations, but also the degree to which the network properties are affected by TMS, via any observed changes in functional connectivity.Different approaches exist to combine TMS and functional imaging according to the temporal order of the methods. Functional MRI can be applied before, during, after, or both before and after TMS. Recently, some studies interleaved TMS and fMRI in order to provide online mapping of the functional changes induced by TMS ^5-7. However, this online combination has many technical problems, including the static artifacts resulting from the presence of the TMS coil in the scanner room, or the effects of TMS pulses on the process of MR image formation. But more importantly, the loud acoustic noise induced by TMS (increased compared with standard use because of the resonance of the scanner bore) and the increased TMS coil vibrations (caused by the strong mechanical forces due to the static magnetic field of the MR scanner) constitute a crucial problem when studying auditory processing. This is one reason why fMRI was carried out before and after TMS in the present study. Similar approaches have been used to target the motor cortex ^8,9, premotor cortex ¹⁰, primary somatosensory cortex ^11,12 and language-related areas ¹³, but so far no combined TMS-fMRI study has investigated the auditory cortex. The purpose of this article is to provide details concerning the protocol and considerations necessary to successfully combine these two neuroscientific tools to investigate auditory processing. Previously we showed that repetitive TMS (rTMS) at high and low frequencies (resp. 10 Hz and 1 Hz) applied over the auditory cortex modulated response time (RT) in a melody discrimination task ². We also showed that RT modulation was correlated with functional connectivity in the auditory network assessed using fMRI: the higher the functional connectivity between left and right auditory cortices during task performance, the higher the facilitatory effect (i.e. decreased RT) observed with rTMS. However those findings were mainly correlational, as fMRI was performed before rTMS. Here, fMRI was carried out before and immediately after TMS to provide direct measures of the functional organization of the auditory cortex, and more specifically of the plastic reorganization of the auditory neural network occurring after the neural intervention provided by TMS. Combined fMRI and TMS applied over the auditory cortex should enable a better understanding of brain mechanisms of auditory processing, providing physiological information about functional effects of TMS. This knowledge could be useful for many cognitive neuroscience applications, as well as for optimizing therapeutic applications of TMS, particularly in auditory-related disorders.     

基于fMRI的脑功能整合数据分析方法综述

脑功能成像在人脑信息处理和认知活动的神经关联中发挥了不可轻视的作用.从大脑功能整合出发,可以将脑功能成像数据分析方法分为探测大脑功能整合的功能连接和有效连接两方面,功能连接探究空间远离的两个脑区之间的连接,有效连接研究一个脑区对另一个脑区作用的大小.根据这两个概念,相应地可以将功能磁共振数据分析方法分为两大类.本文着重...     

Individual differences in white-matter microstructure reflect variation in functional connectivity during choice

The relation between brain structure and function is of fundamental importance in neuroscience. Comparisons between behavioral and brain-imaging measures suggest that variation in brain structure correlates with the presence of specific skills. Behavioral measures, however, reflect the integrated function of multiple brain regions. Rather than behavior, a physiological index of function could be a more sensitive and informative measure with which to compare structural measures. Here, we test for a relationship between a physiological measure of functional connectivity between two brain areas during a simple decision-making task and a measure of structural connectivity. Paired-pulse transcranial magnetic stimulation indexed functional connectivity between two regions important for action choices: the premotor and motor cortex. Fractional anisotropy (FA), a marker of microstructural integrity, indexed structural connectivity. Individual differences in functional connectivity during action selection show highly specific correlations with FA in localized regions of white-matter interconnecting regions, including the premotor and motor cortex. Probabilistic tractography, a technique for identifying fiber pathways from diffusion-weighted imaging (DWI), was used to reconstruct the anatomical networks linking the component brain regions involved in making decisions. These findings demonstrate a relationship between individual differences in functional and structural connectivity within human brain networks central to action choice.     

Multimodal Brain Connectivity Analysis in Unmedicated Late-Life Depression

Late-life depression (LLD) is a common disorder associated with emotional distress, cognitive impairment and somatic complains. Structural abnormalities have been suggested as one of the main neurobiological correlates in LLD. However the relationship between these structural abnormalities and altered functional brain networks in LLD remains poorly understood. 15 healthy elderly comparison subjects from the community and 10 unmedicated and symptomatic subjects with geriatric depression were selected for this study. For each subject, 87 regions of interest (ROI) were generated from whole brain anatomical parcellation of resting state fMRI data. Whole-brain ROI-wise correlations were calculated and compared between groups. Group differences were assessed using an analysis of covariance after controlling for age, sex and education with multiple comparison correction using the false discovery rate. Structural connectivity was assessed by tract-based spatial statistics (TBSS). LLD subjects had significantly decreased connectivity between the right accumbens area (rA) and the right medial orbitofrontal cortex (rmOFC) as well as between the right rostral anterior cingulate cortex (rrACC) and bilateral superior frontal gyrus (bsSFG). Altered connectivity of rrACC with the bsSFG was significantly correlated with depression severity in depressed subjects. TBSS analysis showed a 20% reduction in fractional anisotropy (FA) in the right Forceps Minor (rFM) in depressed subjects. rFM FA values were positively correlated with rA-rmOFC and rrACC-bsFG functional connectivity values in our total study sample. Coordinated structural and functional impairment in circuits involved in emotion regulation and reward pathways play an important role in the pathophysiology of LLD.     

Functional interactions of the inferior frontal cortex during the processing of words and word-like stimuli

The hypothesis that ventral/anterior left inferior frontal gyrus (LIFG) subserves semantic processing and dorsal/posterior LIFG subserves phonological processing was tested by determining the pattern of functional connectivity of these regions with regions in left occipital and temporal cortex during the processing of words and word-like stimuli. In accordance with the hypothesis, we found strong functional connectivity between activity in ventral LIFG and activity in occipital and temporal cortex only for words, and strong functional connectivity between activity in dorsal LIFG and activity in occipital and temporal cortex for words, pseudowords, and letter strings, but not for false font strings. These results demonstrate a task-dependent functional fractionation of the LIFG in terms of its functional links with posterior brain areas.     

Bilinear dynamical systems

In this paper, we propose the use of bilinear dynamical systems (BDS)s for model-based deconvolution of fMRI time-series. The importance of this work lies in being able to deconvolve haemodynamic time-series, in an informed way, to disclose the underlying neuronal activity. Being able to estimate neuronal responses in a particular brain region is fundamental for many models of functional integration and connectivity in the brain. BDSs comprise a stochastic bilinear neurodynamical model specified in discrete time, and a set of linear convolution kernels for the haemodynamics. We derive an expectation-maximization (EM) algorithm for parameter estimation, in which fMRI time-series are deconvolved in an E-step and model parameters are updated in an M-Step. We report preliminary results that focus on the assumed stochastic nature of the neurodynamic model and compare the method to Wiener deconvolution.     

Investigations into resting-state connectivity using independent component analysis

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.