Correlated Brain Activity Research Articles

Brain Activity Measured by Functional Magnetic Resonance Imaging is Related to Patient Reported Urgency Urinary Incontinence Severity

We investigated the relationship between experimental neuroimaging and self-reported urinary incontinence measures. We evaluated 14 functionally independent, community dwelling women older than 60 years with moderate to severe urgency urinary incontinence. All underwent detailed clinical assessment (3-day bladder diary, 24-hour pad test and quality of life assessment), urodynamic testing and functional brain scanning. Brain activity during reported urgency was assessed using a method that combines functional magnetic resonance imaging with simultaneous urodynamic monitoring during repeat bladder filling/emptying cycles. We used the statistical parametric mapping program SPM2 (http://www.fil.ion.ucl.ac.uk/spm/spm2.html) to correlate brain activity with relevant clinical covariates, including the number of urgency incontinent episodes, amount of urine leakage and psychological burden as assessed by the Urge Impact Scale questionnaire. Activity in rostral and subgenual anterior cingulate gyrus, insula, inferior frontal gyrus, orbitofrontal cortex, dorsal and posterior cingulate gyrus, parahippocampus, cuneus and parts of parietotemporal lobe correlated positively with daytime incontinence frequency and urine loss. Different brain regions correlated with the psychological burden and the associations were inverse, that is precuneus/cuneus and posterior cingulate gyrus, and superior temporal, supramarginal and transverse gyrus. As provoked by bladder filling, regional brain activity in the setting of self-reported urgency correlates significantly with incontinence severity in daily life and the associated psychological burden. Thus, observations made under experimental conditions correlate with patient real-life experience and suggest neural correlates of urgency incontinence symptoms that could serve as potential targets for future investigations.

Reconstruction of correlated brain activity with adaptive spatial filters in MEG

Adaptive spatial filters (beamformers) have gained popularity as an effective method for the localization of brain activity from magnetoencephalography (MEG) data. Among the attractive features of some beamforming methods are high spatial resolution and no localization bias even in the presence of random noise. A drawback common to all beamforming methods, however, is significant degradation in performance in the presence of sources with high temporal correlations. Using numerical simulations and examples of auditory and visual evoked field responses, we demonstrate that, at typical signal-to-noise levels, the complete attenuation of fully correlated brain activity is less likely to occur, although significant localization and amplitude biases may occur. We compared various methods for correcting these biases and found the coherent source suppression model (CSSM) (Dalal et al., 2006) to be the most effective, with small biases for widely separated sources (e.g., bilateral auditory areas), however, amplitude biases increased systematically as distance between the sources was decreased. We assessed the performance and systematic biases that may result from the use of this model, and confirmed our findings with real examples of correlated brain activity in bilateral occipital and inferior temporal areas evoked by visually presented faces in a group of 21 adults. We demonstrated the ability to localize source activity in both regions, including correlated sources that are in close proximity (∼3 cm) in bilateral primary visual cortex when using a priori information regarding source location. We conclude that CSSM, when carefully applied, can significantly improve localization accuracy, although amplitude biases may remain.

Functional Connectivity of the Macaque Brain across Stimulus and Arousal States

Cortical networks generate temporally correlated brain activity. To clarify the functional significance of this correlated activity, we asked whether and how its structure depends on stimulus and arousal state. Using independent components analysis of macaque functional magnetic resonance imaging data, we identified a large number of brain networks that were strikingly reproducible across different visual stimulus contexts. Fewer networks were reproducible across alert and anesthetized brain states. Network complexity ranged from bilateral single-node networks to networks comprising multiple discrete nodes distributed over 3 cm of cortex; one network identified in our survey included parts of the temporal parietal occipital junction, dorsal premotor cortex, insula, and posterior cingulate cortex bilaterally. Our results reveal the wealth of spatially structured correlated networks throughout the brain in both alert and anesthetized monkeys, and show that anesthesia significantly alters the spatial structure of these networks.

Community structure and modularity in networks of correlated brain activity

Functional connectivity patterns derived from neuroimaging data may be represented as graphs or networks, with individual image voxels or anatomically-defined structures representing the nodes, and a measure of correlation between the responses in each pair of nodes determining the edges. This explicit network representation allows network-analysis approaches to be applied to the characterization of functional connections within the brain. Much recent research in complex networks has focused on methods to identify community structure, i.e. cohesive clusters of strongly interconnected nodes. One class of such algorithms determines a partition of a network into 'sub-networks' based on the optimization of a modularity parameter, thus also providing a measure of the degree of segregation versus integration in the full network. Here, we demonstrate that a community structure algorithm based on the maximization of modularity, applied to a functional connectivity network calculated from the responses to acute fluoxetine challenge in the rat, can identify communities whose distributions correspond to anatomically meaningful structures and include compelling functional subdivisions in the brain. We also discuss the biological interpretation of the modularity parameter in terms of segregation and integration of brain function.

Akustische Halluzinationen und funktionelle Bildgebung

Auditory verbal hallucinations are a common symptom of schizophrenia. In general, hallucinations can affect all sensory modalities and occur in many neuropsychiatric disorders. They also serve the psychology of perception as the classic example of sensory experience in the absence of adequate external stimuli. Functional imaging studies showed the auditory cortex, the limbic system and language areas, both motor and sensory, to be active during auditory hallucinations. The psychological and neurophysiological models of hallucination can be integrated if we consider that patients with schizophrenia might ascribe internal monologues or dialogues to external sources. The activity of language areas during hallucinations would conform to such a model while the activity in auditory cortex might explain why auditory hallucinations are often so vivid and real for the patients suffering from them. Moreover, the activation of the limbic system might correspond to the emotional aspects of the content of the voices and the accompanying arousal. While the neurophysiological models of hallucination are thus already rather refined, the attempt at suppressing auditory cortex activity with repetitive transcranial magnetic stimulation in order to alleviate treatment-resistant acoustic hallucinations, which is based on the functional imaging findings, still needs further study. Treatment schemes that are based on the psychological theories are more varied and have shown more consistent and long lasting effects but also suffer from the difficulty in measuring hallucinations quantitatively. Future research with functional and structural imaging should go beyond correlating brain activity and symptoms and also address the functional and structural connectivity patterns in the brain that enable hallucinations.

Efficient Localization of Synchronous EEG Source Activities Using a Modified RAP-MUSIC Algorithm

Synchronization across different brain regions is suggested to be a possible mechanism for functional integration. Noninvasive analysis of the synchronization among cortical areas is possible if the electrical sources can be estimated by solving the electroencephalography inverse problem. Among various inverse algorithms, spatio-temporal dipole fitting methods such as RAP-MUSIC and R-MUSIC have demonstrated superior ability in the localization of a restricted number of independent sources, and also have the ability to reliably reproduce temporal waveforms. However, these algorithms experience difficulty in reconstructing multiple correlated sources. Accurate reconstruction of correlated brain activities is critical in synchronization analysis. In this study, we modified the well-known inverse algorithm RAP-MUSIC to a multistage process which analyzes the correlation of candidate sources and searches for independent topographies (ITs) among precorrelated groups. Comparative studies were carried out on both simulated data and clinical seizure data. The results demonstrated superior performance with the modified algorithm compared to the original RAP-MUSIC in recovering synchronous sources and localizing the epileptiform activity. The modified RAP-MUSIC algorithm, thus, has potential in neurological applications involving significant synchronous brain activities.

Cortical Activity and the Explanatory Gap

An exploration is given of neural network features now being uncovered in cortical processing which begins to go a little way to help bridge the “Explanatory Gap” between phenomenal consciousness and correlated brain activity. A survey of properties suggested as being possessed by phenomenal consciousness leads to a set of criteria to be required of the correlated neural activity. Various neural styles of processing are reviewed and those fitting the criteria are selected for further analysis. One particular processing style, in which semiautonomous and long-lasting cortical activity “bubbles” are created by input, is selected as being the most appropriate. Further experimental criteria are used to help narrow the possible neural styles involved. This leads to a class of neural models underpinning phenomenal consciousness and to a related set of testable predictions.

Roger Wolcott Sperry. 20 August 1913—17 April 1994

‘Where does behaviour come from? What is the purpose of consciousness?’ Questions such as these, which appeared on the first page of Sperry's class notes in a freshman psychology course at Oberlin College, represent an accurate preview of a career that included major contributions to fundamental issues in neurobiology, psychology and philosophy. Indeed, his first paper, published in the Journal of General Psychology in 1939, entitled ‘Action current study in movement coordination’ (1)*, begins: ‘The objective psychologist, hoping to get at the physiological side of behavior, is apt to plunge immediately into neurology trying to correlate brain activity with modes of experience’, and continues, setting the stage for much that was to follow: ‘The result in many cases only accentuates the gap between the total experience as studied by the psychologist and neural activity as analyzed by the neurologist.’