Example of quantitative high resolution (2x2x2 mm) ASL at 3T

In a series of articles, we will attempt to clarify how some aspects of the concepts of functional and effective connectivity, as applied to functional brain imaging data, are to be understood in terms of neuroanatomy and neurophysiology. In this first paper, we will focus primarily on one of the haemodynamic methods (fMRI; however, because many of the issues discussed here apply as well to PET, we will also include some discussion applicable to this technique); subsequent papers will address other aspects of functional/effective connectivity, as well as other methods for assessing neural interactions. As is well known, the limited spatial and temporal resolution of the haemodynamic functional imaging techniques results in a significant loss of information as one goes from the microscopic level of dynamic neuronal activity to the macroscopic level changes in regional cerebral blood flow (rCBF), metabolism and blood oxygenation (BOLD) measured by PET/fMRI (a brief overview of PET and fMRI methodology can be found in ; for more information about fMRI, see ). Of the factors that result in this loss of information, the following are particularly important: (i) multiple neuronal populations are present in any resolvable PET or fMRI region of interest (including a single voxel), and local and afferent neuronal activities are combined into a single signal; (ii) the disparity between the temporal dimension appropriate for neurons (on the order of milliseconds) and that available from haemodynamic data (about a minute for PET, at best a few seconds for fMRI owing to the haemodynamic delay) is such that transient components of activity are undetectable by the haemodynamic methods; (iii) electrophysiological studies at the neuronal level generally record the spiking activity of neurons, whereas the evidence is now fairly substantial that the haemodynamic methods are indicative of synaptic and postsynaptic activity (e.g. ; ; ); one consequence of this is that increases in excitatory and inhibitory synaptic activity can lead to increased metabolic activity ().

Functional and Effective Connectivity in ..

(2015) Network structure shapes spontaneous functional connectivity dynamics.

Friston KJ (1994) Functional and effective connectivity …

In this paper we will use the large-scale computational model outlined above to examine the relationship between the macroscopic measures of functional and effective connectivity (as obtained from functional neuroimaging data) and their neural substrates. We will begin with a concise overview of the concepts of functional and effective connectivity, as applied by various neuroscientific methods of investigation. We shall then briefly review the large-scale network model. This will be followed by a discussion of the sources of variability that are used in functional (and effective) connectivity analysis, and a description of how these variability sources will be simulated in our model. Our main results will ensue, followed by some comments about how these concepts can be interpreted. Our focus in this paper will be on functional connectivity as used in fMRI and PET (and also deoxyglucose autoradiographic studies). In subsequent papers, we will address some of the relatively newer notions related to effective connectivity (e.g. structural equation modelling (); Granger causality (; ); dynamic causal modelling ()) that have recently emerged, as well as examining measures of functional connectivity used with EEG/MEG data.

Functional and Effective Connectivity a Review | …

For decades investigators have attempted to use neurophysiological data obtained simultaneously from two or more neural elements to compute a measure of the functional interaction between these elements. The neural elements could be neurons, small ensembles of neurons, or entire brain regions, and the data could be obtained using electrical, magnetic or haemodynamic techniques. In all cases, the central idea is that activities that covary together suggest that the neurons generating the activities may be interacting. Two aspects of functional interactivity need to be distinguished; they are called functional and effective connectivity (). Two neural entities are said to be functionally connected if their activities are correlated; effective connectivity refers to the direct influence of one neural entity on a second. Thus, functional connectivity does not necessarily imply a causal link, whereas effective connectivity does. As pointed out by Friston (see ), effective connectivity is model dependent, whereas functional connectivity is not (at least not explicitly; note, however, that in the evaluation of functional connectivity as a simple correlation coefficient, it is implicitly assumed that the interactions are linear and instantaneous; see and for approaches that avoid these assumptions). It should be noted that different investigators have, over the years, used different terms to represent these two notions, but the functional neuroimaging community seems to have settled on these general designations ().

(2013) The relationship between level of processing and hippocampal–cortical functional connectivity during episodic memory formation in humans.
(2016) Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting‐state functional connectivity.

Functional connectivity and effective ..

Therefore, we employ a large-scale, neurobiologically plausible computational model that can generate both simulated neuronal activities, and simulated PET and fMRI data (; ; , ). The model performs a delayed matched-to-sample (DMS) task. Two forms of this model exist—one for visual object processing (), the second for auditory object processing (). Furthermore, the visual model was extended so that combined transcranial magnetic stimulation (TMS) and PET studies could be simulated (). TMS has been used in conjunction with PET as a tool for investigating functional connectivity (; ; ). Recently, studies combining TMS and fMRI have been reported (; ; ). The model, whose construction was based on experimental neuroanatomical and neurophysiological measurements, generates simulated neuronal data that agree with experimental data obtained from mammalian studies, and also generates simulated PET/fMRI data that are in accord with experimental findings from human studies. Unlike some simulations that have been employed to examine the ‘neural’ substrate of a functional neuroimaging result (e.g. ; ), our model has the advantage that it is neurally plausible, complex, contains both excitatory and inhibitory neurons, has feed-forward and feedback connections and includes a diversity of regions containing neurons that possess different response properties (e.g. some regions have neurons that fire only when external stimuli are present; others have neurons that are active when no external stimuli are present). This type of model provides a useful testing ground for investigating both experimental paradigms and data analysis methods ().

01/10/2001 · Functional and Effective Connectivity in Neuroimaging: A Synthesis

Functional and effective connectivity: A synthesis

The excavation and reburial of even a small landfill site can be very expensive. For example, the estimated reburial cost for a landfill like that shown in Figure 3-11 was in excess of $ 4 million in 1978.

Friston KJ (1994) Functional and effective connectivity in neuroimaging: a synthesis

Functional and effective connectivity: a synthesis

Accumulating evidence suggests that the small-world topological properties of brain functional networks are altered in patients with schizophrenia. In one study, in 31 patients with schizophrenia compared with 31 healthy controls, functional connectivity between 90 cortical and subcortical regions was estimated by partial correlation analysis and thresholded to construct a set of unidirected graphs. The healthy subjects demonstrated efficient small-world properties, whereas topological parameters of brain networks — strength and degree of connectivity — were decreased in patients with schizophrenia, especially in the prefrontal, parietal, and temporal lobes, consistent with a hypothesis of dysfunctional integration. In another study, in a sample of 203 patients with schizophrenia, compared with 259 healthy controls, multimodal network organization was noted to be abnormal, as measured by topological and distance metrics of anatomical network organization, abstracted from fMRI data. Patients with schizophrenia, compared with controls, demonstrated reduced hierarchy throughout the small-world regime, and increased connection distance in the multimodal cortical network. The loss of frontal hubs and the emergence of nonfrontal hubs was also noted, supporting the hypothesis of schizophrenia as a dysconnectivity syndrome, impacting the efficiency of a frontally dominated hierarchical network of multimodal cortical connections. Though the impact of genetic variation on network topology based on graph analyses has not yet been reported, moderate levels of heritability have been found for brain graph topology measured in a twin study using EEG, suggesting that genetic variation may Impact small-world organization and brain graph metrics.