Subject specificity of the correlation between large-scale structural and functional connectivity.

Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual's SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.

Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models.

Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, "The Virtual Brain (TVB)", based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

fMRI evidence of equivalent neural suppression by repetition and prior knowledge.

Stimulus repetition speeds behavioral responding (behavioral priming) and is accompanied by suppressed neural responses (repetition suppression; RS) that have been observed up to three days after initial exposure. While some proposals have suggested the two phenomena are linked, behavioral priming has been observed many years after initial exposure, whereas RS is widely considered a transitory phenomenon. This raises the question: what is the true upper limit of RS persistence? To answer this question, we scanned healthy, English-native adults with fMRI as they viewed novel (Asian) proverbs, recently repeated (Asian) proverbs, and previously known (English) proverbs that were matched on various dimensions. We then estimated RS by comparing repeated or previously known proverbs against novel ones. Multivariate analyses linked previously known and repeated proverbs with statistically indistinguishable RS in a broad visual-linguistic network. In each suppressed region, prior knowledge and repetition also induced a common shift in functional connectivity, further underscoring the similarity of the RS phenomenon induced by these conditions. By contrast, activated regions readily distinguished prior knowledge and repetition conditions in a manner consistent with engagement of semantic and episodic memory systems, respectively. Our results illustrate that regardless of whether RS is understood in terms of its magnitude, spatial extent or functional connectivity profile, typical RS effects can be elicited even under conditions where recently triggered biological processes or episodic memory are unlikely to play a prominent role. These results provide important new evidence that RS (of the kind observed after an interval of at least several minutes) reflects the facilitation of perceptual and comprehension processes by any type of information retrieved from long-term memory.

Trap-shyness subsidence is a threshold function of mark-recapture interval in brown mudfish Neochanna apoda populations.

The influence of capture interval on trap shyness, and temperature, rainfall and drought on capture probability (p) in 827 brown mudfish Neochanna apoda was quantified using mark-recapture models. In particular, it was hypothesized that the loss of trapping memory in marked N. apoda would lead to a capture-interval threshold required to minimize trap shyness. Neochanna apoda trap shyness approximated a threshold response to capture interval, declining rapidly with increasing capture intervals up to 16.5 days, after which p remained constant. Tests for detecting trap-dependent capture probability in Cormack-Jolly-Seber models failed to detect trap shyness in N. apoda capture histories with capture intervals averaging 16 days. This confirmed the applicability of the 16 day capture-interval threshold for mark-recapture studies. Instead, N. apoda p was positively influenced by water temperature and rainfall during capture. These results imply that a threshold capture interval is required to minimize the trade-off between the competing assumptions of population closure and p homogeneity between capture occasions in closed mark-recapture models. Moreover, environmental factors that influence behaviour could potentially confound abundance indices, and consequently abundance trends should be interpreted with caution in the face of long-term climate change, such as with global warming.

Spatiotemporal dependency of age-related changes in brain signal variability.

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18-72) and one magnetoencephalography (n = 31, ages 20-75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence.

Towards the virtual brain: network modeling of the intact and the damaged brain.

Neurocomputational models of large-scale brain dynamics utilizing realistic connectivity matrices have advanced our understanding of the operational network principles in the brain. In particular, spontaneous or resting state activity has been studied on various scales of spatial and temporal organization including those that relate to physiological, encephalographic and hemodynamic data. In this article we focus on the brain from the perspective of a dynamic network and discuss the role of its network constituents in shaping brain dynamics. These constituents include the brain's structural connectivity, the population dynamics of its network nodes and the time delays involved in signal transmission. In addition, no discussion of brain dynamics would be complete without considering noise and stochastic effects. In fact, there is mounting evidence that the interaction between noise and dynamics plays an important functional role in shaping key brain processes. In particular, we discuss a unifying theoretical framework that explains how structured spatio-temporal resting state patterns emerge from noise driven explorations of unstable or stable oscillatory states. Embracing this perspective, we explore the consequences of network manipulations to understand some of the brain's dysfunctions, as well as network effects that offer new insights into routes towards therapy, recovery and brain repair. These collective insights will be at the core of a new computational environment, the Virtual Brain, which will allow flexible incorporation of empirical data constraining the brain models to integrate, unify and predict network responses to incipient pathological processes.

The development of a noisy brain.

Early in life, brain development carries with it a large number of structural changes that impact the functional interactions of distributed neuronal networks. Such changes enhance information processing capacity, moving the brain from a deterministic system to one that is more stochastic. The evidence from empirical studies with EEG and functional MRI suggests that this stochastic property is a result of an increased number of possible functional network configurations for a given situation. This is captured in the variability of endogenous and evoked responses or "brain noise ". In empirical data from infants and children, brain noise increases with maturation and correlates positively with stable behavior and accuracy. The noise increase is best explained through increased noise from network level interactions with a concomitant decrease of local noise. In old adults, brain noise continues to change, although the pattern of changes is not as global as in early development. The relation between high brain noise and stable behavior is maintained, but the relationships differ by region, suggesting changes in local dynamics that then impact potential network configurations. These data, when considered in concert with our extant modeling work, suggest that maturational changes in brain noise represent the enhancement offunctional network potential--the brain's dynamic repertoire.

Encoding the future: successful processing of intentions engages predictive brain networks.

Evidence from cognitive, patient and neuroimaging research indicates that "remembering to remember" intentions, i.e., prospective memory (PM) retrieval, requires both general memory systems involving the medial temporal lobes and an executive system involving rostral PFC (BA 10). However, it is not known how prospective memories are initially formed. Using fMRI, we investigated whether brain activity during encoding of future intentions and present actions differentially predicted later memory for those same intentions (PM) and actions (retrospective memory). We identified two significant patterns of neural activity: a network linked to overall memory and another linked specifically to PM. While overall memory success was predicted by temporal lobe activations that included the hippocampus, PM success was also uniquely predicted by activations in additional regions, including left rostrolateral PFC and the right parahippocampal gyrus. This finding extends the role of these structures to the formation of individual intentions. It also provides the first evidence that PM encoding, like PM retrieval, is supported by both a common episodic memory network and an executive network specifically recruited by future-oriented processing.

The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models.

Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.

Key role of coupling, delay, and noise in resting brain fluctuations.

A growing body of neuroimaging research has documented that, in the absence of an explicit task, the brain shows temporally coherent activity. This so-called "resting state" activity or, more explicitly, the default-mode network, has been associated with daydreaming, free association, stream of consciousness, or inner rehearsal in humans, but similar patterns have also been found under anesthesia and in monkeys. Spatiotemporal activity patterns in the default-mode network are both complex and consistent, which raises the question whether they are the expression of an interesting cognitive architecture or the consequence of intrinsic network constraints. In numerical simulation, we studied the dynamics of a simplified cortical network using 38 noise-driven (Wilson-Cowan) oscillators, which in isolation remain just below their oscillatory threshold. Time delay coupling based on lengths and strengths of primate corticocortical pathways leads to the emergence of 2 sets of 40-Hz oscillators. The sets showed synchronization that was anticorrelated at <0.1 Hz across the sets in line with a wide range of recent experimental observations. Systematic variation of conduction velocity, coupling strength, and noise level indicate a high sensitivity of emerging synchrony as well as simulated blood flow blood oxygen level-dependent (BOLD) on the underlying parameter values. Optimal sensitivity was observed around conduction velocities of 1-2 m/s, with very weak coupling between oscillators. An additional finding was that the optimal noise level had a characteristic scale, indicating the presence of stochastic resonance, which allows the network dynamics to respond with high sensitivity to changes in diffuse feedback activity.

Cortical network dynamics with time delays reveals functional connectivity in the resting brain.

In absence of all goal-directed behavior, a characteristic network of cortical regions involving prefrontal and cingulate cortices consistently shows temporally coherent fluctuations. The origin of these fluctuations is unknown, but has been hypothesized to be of stochastic nature. In the present paper we test the hypothesis that time delays in the network dynamics play a crucial role in the generation of these fluctuations. By tuning the propagation velocity in a network based on primate connectivity, we scale the time delays and demonstrate the emergence of the resting state networks for biophysically realistic parameters.

Noise during rest enables the exploration of the brain's dynamic repertoire.

Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

Why is the meaning of a sentence better remembered than its form? An fMRI study on the role of novelty-encoding processes.

Episodic memory is based primarily on meaning. This is behaviorally well documented in studies on memory for prose, in which the meaning of novel sentences is typically well remembered but information pertaining to exact wording and syntax is not. The neural basis of this 'verbatim effect' is poorly understood. In the current fMRI study, we manipulated the novelty of sentences at different levels to test whether medial temporal lobe (MTL) regions that are known to play a critical role in verbal episodic encoding would respond preferentially to the novelty of sentence meaning. Fifteen participants were pre-familiarized with auditory sentences describing unique episodes. During scanning, they encountered sentences that were old, that contained a change in (i.e., were novel in terms of) syntactic relationships, that contained a change in semantic relationships, or that described an entirely novel episode. Subsequently, participants performed a recognition memory test for the different types of novel information encountered. Behavioral data confirmed the typical verbatim effect. Analyses of fMRI data revealed differential MTL activation in the left hippocampus and entorhinal cortex with a response profile across conditions that paralleled the behavioral results; the identified region responded selectively to those conditions that contained semantic novelty. Other regions, by contrast, showed a novelty response that did not share this selectivity. Our findings suggest that the verbatim effect can be linked to hippocampally-based novelty-assessment processes that operate based on semantic relationships.

Age-related differences in brain activity during verbal recency memory.

In the current event-related fMRI study young and older adults underwent fMRI scanning while performing recognition, recency and reverse alphabetizing tasks. The reverse alphabetizing task served as a control for executive processes, such as working memory manipulation and monitoring (Henson, R.N., Shallice, T., et al., 1999. Right prefrontal cortex and episodic memory retrieval: a functional MRI test of the monitoring hypothesis. Brain 122 (Pt 7), 1367-1381; Dobbins, I.G., Schnyer, D.M., et al., 2004a. Cortical activity reductions during repetition priming can result from rapid response learning. Nature 428 (6980), 316-319; Rajah, M.N., McIntosh, A.R., 2006. Dissociating prefrontal contributions during a recency memory task. Neuropsychologia 44 (3), 350-364). Multivariate spatio-temporal partial least squares (ST-PLS) analysis was used to identify task-related similarities and differences in regional activity in young versus older adults. The behavioural results indicated that older adults performed disproportionately worse on recency, but not recognition memory, compared to young adults. The fMRI results show the older adults activated right parahippocampal, right parietal, left precuneus and right prefrontal regions to a greater degree during both recognition and recency retrieval, compared to young adults. Brain-behaviour correlation analysis showed that increased activity in right parahippocampal and parietal cortex was related to poorer retrieval performance in older adults, but was related to improved recency accuracy and reverse alphabetizing accuracy in young adults, respectively. In contrast, the age-related increase in right prefrontal and left precuneus activity was related to improved recognition, but not recency, performance in older adults. In young adults, activity in these regions was not strongly related to retrieval performance. These results suggest that older adults exhibited deficits in medial temporal and parietal function during retrieval, which was functionally compensated for by increased recruitment of prefrontal and precuneus regions. This functional compensation was sufficient for maintaining recognition but not recency retrieval in older adults.

Dissociating prefrontal contributions during a recency memory task.

Neuroimaging studies of normal young adults have consistently found right prefrontal cortex (RPFC) activity during the performance of recency memory tasks. However, it is unclear whether the involvement of RPFC during these tasks reflects retrieval processes or executive processes such as: strategic ordering or monitoring. In the current study, we distinguish between those PFC regions that are more related to retrieval processes, versus strategic ordering processes. An event-related fMRI study was conducted in which eight young subjects were scanned while performing verbal episodic retrieval tasks (recognition and recency memory tasks), and verbal non-memory strategic organizing control tasks (reverse alphabetizing of words). The fMRI results show that young subjects engaged right dorsolateral PFC during recency and reverse alphabetizing control tasks. Left ventral PFC was engaged across all tasks; however, a subset of voxels within this region was more active during retrieval tasks. Left dorsolateral and right ventral PFC activity was more related to the performance of reverse alphabetizing tasks, respectively. We conclude that right dorsolateral PFC activity during recency memory reflects more general strategic organizational or monitoring processes, and is not EM-specific.

Overlap in the functional neural systems involved in semantic and episodic memory retrieval.

Neuroimaging and neuropsychological data suggest that episodic and semantic memory may be mediated by distinct neural systems. However, an alternative perspective is that episodic and semantic memory represent different modes of processing within a single declarative memory system. To examine whether the multiple or the unitary system view better represents the data we conducted a network analysis using multivariate partial least squares (PLS ) activation analysis followed by covariance structural equation modeling (SEM) of positron emission tomography data obtained while healthy adults performed episodic and semantic verbal retrieval tasks. It is argued that if performance of episodic and semantic retrieval tasks are mediated by different memory systems, then there should differences in both regional activations and interregional correlations related to each type of retrieval task, respectively. The PLS results identified brain regions that were differentially active during episodic retrieval versus semantic retrieval. Regions that showed maximal differences in regional activity between episodic retrieval tasks were used to construct separate functional models for episodic and semantic retrieval. Omnibus tests of these functional models failed to find a significant difference across tasks for both functional models. The pattern of path coefficients for the episodic retrieval model were not different across tasks, nor were the path coefficients for the semantic retrieval model. The SEM results suggest that the same memory network/system was engaged across tasks, given the similarities in path coefficients. Therefore, activation differences between episodic and semantic retrieval may ref lect variation along a continuum of processing during task performance within the context of a single memory system.

Spatiotemporal analysis of event-related fMRI data using partial least squares.

Partial least squares (PLS) has proven to be a important multivariate analytic tool for positron emission tomographic and, more recently, event-related potential (ERP) data. The application to ERP incorporates the ability to analyze space and time together, a feature that has obvious appeal for event-related functional magnetic resonance imaging (fMRI) data. This paper presents the extension of spatiotemporal PLS (ST-PLS) to fMRI, explaining the theoretical foundation and application to an fMRI study of auditory and visual perceptual memory. Analysis of activation effects with ST-PLS was compared with conventional univariate random effects analysis, showing general consensus for both methods, but several unique observations by ST-PLS, including enhanced statistical power. The application of ST-PLS for assessment of task-dependent brain-behavior relationships is also presented. Singular features of ST-PLS include (1) no assumptions about the shape of the hemodynamic response functions (HRFs); (2) robust statistical assessment at the image level through permutation tests; (3) protection against outlier influences at the voxel level through bootstrap resampling; (4) flexible analytic configurations that allow assessment of activation difference, brain-behavior relations, and functional connectivity. These features enable ST-PLS to act as an important complement to other multivariate and univariate approaches used in neuroimaging research.

Limbic-frontal circuitry in major depression: a path modeling metanalysis.

This paper reports the results of an across lab metanalysis of effective connectivity in major depression (MDD). Using FDG PET data and Structural Equation Modeling, a formal depression model was created to explicitly test current theories of limbic-cortical dysfunction in MDD and to characterize at the path level potential sources of baseline variability reported in this patient population. A 7-region model consisting of lateral prefrontal cortex (latF9), anterior thalamus (aTh), anterior cingulate (Cg24), subgenual cingulate (Cg25), orbital frontal cortex (OF11), hippocampus (Hc), and medial frontal cortex (mF10) was tested in scans of 119 depressed patients and 42 healthy control subjects acquired during three separate studies at two different institutions. A single model, based on previous theory and supported by anatomical connectivity literature, was stable for the three groups of depressed patients. Within the context of this model, path differences among groups as a function of treatment response characteristics were also identified. First, limbic-cortical connections (latF9-Cg25-OF11-Hc) differentiated drug treatment responders from nonresponders. Second, nonresponders showed additional abnormalities in limbic-subcortical pathways (aTh-Cg24-Cg25-OF11-Hc). Lastly, more limited limbic-cortical (Hc-latF9) and cortical-cortical (OF11-mF10) path differences differentiated responders to cognitive behavioral therapy (CBT) from responders to pharmacotherapy. We conclude that the creation of such models is a first step toward full characterization of the depression phenotype at the neural systems level, with implications for the future development of brain-based algorithms to determine optimal treatment selection for individual patients.