Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept (''binding'') is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto-or electroencephalography (MEG͞EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.oscillations ͉ functional connectivity ͉ coherence ͉ magnetoencephalography ͉ synchronization T he hypothesis that relevant information in the brain is coded by accurate timing of neuronal discharges has received strong support from recent reports of synchronization of neuronal firing within and across areas of the cat visual cortex (1). The synchronization of neural activity, which was modulated by gammaband oscillations, was shown to depend on stimulus properties like continuity, vicinity, and common motion, and on receptive field constellations (for review, see ref.2). This and similar findings seem to support the concept that synchronized rhythmic neural firing has a role in solving the binding problem, i.e., the integration of distributed information into a unified representation (1-4).To investigate cortico-cortical synchrony noninvasively in the human brain, new analysis tools must be developed. In functional magnetic resonance imaging (fMRI) studies, structural equation models have been used to estimate connectivities between brain areas (5, 6). Although this is a very promising approach, it lacks the temporal resolution required to measure oscillatory activity and to observe the expected transient formation of neuronal assemblies (7).Magnetoencephalography (MEG) and electroencephalography (...

A neuroimaging study reveals how coupled brain oscillations at different frequencies align with quasi-rhythmic features of continuous speech such as prosody, syllables, and phonemes.

The huge number of neurons in the human brain are connected to form functionally specialized assemblies. The brain's amazing processing capabilities rest on local communication within and long-range communication between these assemblies. Even simple sensory, motor and cognitive tasks depend on the precise coordination of many brain areas. Recent improvements in the methods of studying long-range communication have allowed us to address several important questions. What are the common mechanisms that govern local and long-range communication and how do they relate to the structure of the brain? How does oscillatory synchronization subserve neural communication? And what are the consequences of abnormal synchronization?

A growing body of evidence shows that ongoing oscillations in auditory cortex modulate their phase to match the rhythm of temporally regular acoustic stimuli, increasing sensitivity to relevant environmental cues and improving detection accuracy. In the current study, we test the hypothesis that nonsensory information provided by linguistic content enhances phase-locked responses to intelligible speech in the human brain. Sixteen adults listened to meaningful sentences while we recorded neural activity using magnetoencephalography. Stimuli were processed using a noise-vocoding technique to vary intelligibility while keeping the temporal acoustic envelope consistent. We show that the acoustic envelopes of sentences contain most power between 4 and 7 Hz and that it is in this frequency band that phase locking between neural activity and envelopes is strongest. Bilateral oscillatory neural activity phase-locked to unintelligible speech, but this cerebro-acoustic phase locking was enhanced when speech was intelligible. This enhanced phase locking was left lateralized and localized to left temporal cortex. Together, our results demonstrate that entrainment to connected speech does not only depend on acoustic characteristics, but is also affected by listeners’ ability to extract linguistic information. This suggests a biological framework for speech comprehension in which acoustic and linguistic cues reciprocally aid in stimulus prediction.

Interactions between functionally specialized brain regions are crucial for normal brain function. Magnetoencephalography (MEG) and electroencephalography (EEG) are techniques suited to capture these interactions, because they provide whole head measurements of brain activity in the millisecond range. More than one sensor picks up the activity of an underlying source. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. Consequentially, neuronal interactions should be studied on the level of the reconstructed sources. This article reviews several methods that have been applied to investigate interactions between brain regions in source space. We will mainly focus on the different measures used to quantify connectivity, and on the different strategies adopted to identify regions of interest. Despite various successful accounts of MEG and EEG source connectivity, caution with respect to the interpretation of the results is still warranted. This is due to the fact that effects of field spread can never be completely abolished in source space. However, in this very exciting and developing field of research this cautionary note should not discourage researchers from further investigation into the connectivity between neuronal sources.