Maher A. Quraan

Detection and Localization of Hippocampal Activity Using Beamformers with MEG: A Detailed Investigation Using Simulations and Empirical Data

The ability to detect neuronal activity emanating from deep brain structures such as the hippocampus using magnetoencephalography has been debated in the literature. While a significant number of recent publications reported activations from deep brain structures, others reported their inability to detect such activity even when other detection modalities confirmed its presence. In this article, we relied on realistic simulations to show that both sides of this debate are correct and that these findings are reconcilable. We show that the ability to detect such activations in evoked responses depends on the signal strength, the amount of brain noise background, the experimental design parameters, and the methodology used to detect them. Furthermore, we show that small signal strengths require contrasts with control conditions to be detected, particularly in the presence of strong brain noise backgrounds. We focus on one localization technique, the adaptive spatial filter (beamformer), and examine its strengths and weaknesses in reconstructing hippocampal activations, in the presence of other strong brain sources such as visual activations, and compare the performance of the vector and scalar beamformers under such conditions. We show that although a weight‐normalized beamformer combined with a multisphere head model is not biased in the presence of uncorrelated random noise, it can be significantly biased in the presence of correlated brain noise. Furthermore, we show that the vector beamformer performs significantly better than the scalar under such conditions. We corroborate our findings empirically using real data and demonstrate our ability to detect and localize such sources. Hum Brain Mapp, 2011.

Default mode network connectivity indicates episodic memory capacity in mesial temporal lobe epilepsy.

The clinical relevance of resting state functional connectivity in neurologic disorders, including mesial temporal lobe epilepsy (mTLE), remains unclear. This study investigated how connectivity in the default mode network changes with unilateral damage to one of its nodes, the hippocampus (HC), and how such connectivity can be exploited clinically to characterize memory deficits and indicate postsurgical memory change.

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 ( approximately 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.

Speech-induced suppression of evoked auditory fields in children who stutter.

Auditory responses to speech sounds that are self-initiated are suppressed compared to responses to the same speech sounds during passive listening. This phenomenon is referred to as speech-induced suppression, a potentially important feedback-mediated speech-motor control process. In an earlier study, we found that both adults who do and do not stutter demonstrated a reduced amplitude of the auditory M50 and M100 responses to speech during active production relative to passive listening. It is unknown if auditory responses to self-initiated speech-motor acts are suppressed in children or if the phenomenon differs between children who do and do not stutter. As stuttering is a developmental speech disorder, examining speech-induced suppression in children may identify possible neural differences underlying stuttering close to its time of onset. We used magnetoencephalography to determine the presence of speech-induced suppression in children and to characterize the properties of speech-induced suppression in children who stutter. We examined the auditory M50 as this was the earliest robust response reproducible across our child participants and the most likely to reflect a motor-to-auditory relation. Both children who do and do not stutter demonstrated speech-induced suppression of the auditory M50. However, children who stutter had a delayed auditory M50 peak latency to vowel sounds compared to children who do not stutter indicating a possible deficiency in their ability to efficiently integrate auditory speech information for the purpose of establishing neural representations of speech sounds.

Auditory evoked fields to vocalization during passive listening and active generation in adults who stutter.

We used magnetoencephalography to investigate auditory evoked responses to speech vocalizations and non-speech tones in adults who do and do not stutter. Neuromagnetic field patterns were recorded as participants listened to a 1 kHz tone, playback of their own productions of the vowel /i/ and vowel-initial words, and actively generated the vowel /i/ and vowel-initial words. Activation of the auditory cortex at approximately 50 and 100 ms was observed during all tasks. A reduction in the peak amplitudes of the M50 and M100 components was observed during the active generation versus passive listening tasks dependent on the stimuli. Adults who stutter did not differ in the amount of speech-induced auditory suppression relative to fluent speakers. Adults who stutter had shorter M100 latencies for the actively generated speaking tasks in the right hemisphere relative to the left hemisphere but the fluent speakers showed similar latencies across hemispheres. During passive listening tasks, adults who stutter had longer M50 and M100 latencies than fluent speakers. The results suggest that there are timing, rather than amplitude, differences in auditory processing during speech in adults who stutter and are discussed in relation to hypotheses of auditory-motor integration breakdown in stuttering.

Evaluation of multiple-sphere head models for MEG source localization

Magnetoencephalography (MEG) source analysis has largely relied on spherical conductor models of the head to simplify forward calculations of the brains magnetic field. Multiple- (or overlapping, local) sphere models, where an optimal sphere is selected for each sensor, are considered an improvement over single-sphere models and are computationally simpler than realistic models. However, there is limited information available regarding the different methods used to generate these models and their relative accuracy. We describe a variety of single- and multiple-sphere fitting approaches, including a novel method that attempts to minimize the field error. An accurate boundary element method simulation was used to evaluate the relative field measurement error (12% on average) and dipole fit localization bias (3.5 mm) of each model over the entire brain. All spherical models can contribute in the order of 1 cm to the localization bias in regions of the head that depart significantly from a sphere (inferior frontal and temporal). These spherical approximation errors can give rise to larger localization differences when all modeling effects are taken into account and with more complex source configurations or other inverse techniques, as shown with a beamformer example. Results differed noticeably depending on the source location, making it difficult to recommend a fitting method that performs best in general. Given these limitations, it may be advisable to expand the use of realistic head models.

Altered resting state brain dynamics in temporal lobe epilepsy can be observed in spectral power, functional connectivity and graph theory metrics.

Despite a wealth of EEG epilepsy data that accumulated for over half a century, our ability to understand brain dynamics associated with epilepsy remains limited. Using EEG data from 15 controls and 9 left temporal lobe epilepsy (LTLE) patients, in this study we characterize how the dynamics of the healthy brain differ from the “dynamically balanced” state of the brain of epilepsy patients treated with anti-epileptic drugs in the context of resting state. We show that such differences can be observed in band power, synchronization and network measures, as well as deviations from the small world network (SWN) architecture of the healthy brain. The θ (4–7 Hz) and high α (10–13 Hz) bands showed the biggest deviations from healthy controls across various measures. In particular, patients demonstrated significantly higher power and synchronization than controls in the θ band, but lower synchronization and power in the high α band. Furthermore, differences between controls and patients in graph theory metrics revealed deviations from a SWN architecture. In the θ band epilepsy patients showed deviations toward an orderly network, while in the high α band they deviated toward a random network. These findings show that, despite the focal nature of LTLE, the epileptic brain differs in its global network characteristics from the healthy brain. To our knowledge, this is the only study to encompass power, connectivity and graph theory metrics to investigate the reorganization of resting state functional networks in LTLE patients.

EEG power asymmetry and functional connectivity as a marker of treatment effectiveness in DBS surgery for depression.

Recently, deep brain stimulation (DBS) has been evaluated as an experimental therapy for treatment-resistant depression. Although there have been encouraging results in open-label trials, about half of the patients fail to achieve meaningful benefit. Although progress has been made in understanding the neurobiology of MDD, the ability to characterize differences in brain dynamics between those who do and do not benefit from DBS is lacking. In this study, we investigated EEG resting-state data recorded from 12 patients that have undergone DBS surgery. Of those, six patients were classified as responders to DBS, defined as an improvement of 50% or more on the 17-item Hamilton Rating Scale for Depression (HAMD-17). We compared hemispheric frontal theta and parietal alpha power asymmetry and synchronization asymmetry between responders and non-responders. Hemispheric power asymmetry showed statistically significant differences between responders and non-responders with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry was characterized by an increase in frontal theta in the right hemisphere relative to the left combined with an increase in parietal alpha in the left hemisphere relative to the right in non-responders compared with responders. Hemispheric mean synchronization asymmetry showed a statistically significant difference between responders and non-responders in the theta band, with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry resulted from an increase in frontal synchronization in the right hemisphere relative to the left combined with an increase in parietal synchronization in the left hemisphere relative to the right in non-responders compared with responders. Connectivity diagrams revealed long-range differences in frontal/central-parietal connectivity between the two groups in the theta band. This pattern was observed irrespective of whether EEG data were collected with active DBS or with the DBS stimulation turned off, suggesting stable functional and possibly structural modifications that may be attributed to plasticity.

ICA-based artifact correction improves spatial localization of adaptive spatial filters in MEG.

Beamformers are one of the most common inverse models currently used in the estimation of source activity from magnetoencephelography (MEG) data. They rely on a minimization of total power while constraining the gain in the voxel of interest, resulting in the suppression of background noise. Nonetheless, in cases where background noise is strong compared to the source of interest, or when many sources are present, the ability of the beamformer to detect and accurately localize weak sources is reduced. In visual paradigms, two main background sources can substantially impact an accurate estimation of weaker sources. Ocular artifacts are orders of magnitude higher than neural sources making it difficult for the beamformer to effectively suppress them. Primary visual activations also result in strong signals that can impede localization of weak sources. In this paper, we systematically evaluated how neural (visual) and non-neural (eye, heart) sources affect the localization accuracy of frontal and medial temporal sources in visual tasks. These sources are of tremendous interest in learning and memory studies as well as in clinical settings (Alzheimers/epilepsy) and are typically difficult to localize robustly in MEG. Empirical data from two tasks - active learning and control - were used to evaluate our analysis techniques. Global field power calculations showed multiple time periods where active learning was significantly different from response selection with dominant sources converging to the eyes. Extensive leakage of eye activity into frontal and visual that evoked responses into parietal cortices was also observed. Contributions from ocular activity to the reconstructed time series were indiscernible from task-based recruitment of frontal sources in the original data. Removing artifacts (eye movements, cardiac, and muscular) by means of independent component analysis (ICA) led to a significant improvement in detection and localization of frontal and medial temporal sources. We verified our results by using simulations of sources placed in frontal and medial temporal regions with various types of background noise (eye, heart, and visual). We report that the detection and localization accuracy of frontal and medial temporal sources with beamformer techniques is highly dependent on the magnitude and location of background sources and that removing artifacts can substantially improve the beamformers performance.

Hippocampal lateralization and memory in children and adults.

The neural organization of cognitive processes, particularly hemispheric lateralization, changes throughout childhood and adolescence. Differences in the neural basis of relational memory between children and adults are not well characterized. In this study we used magnetoencephalography to observe the lateralization differences of hippocampal activation in children and adults during performance of a relational memory task, transverse patterning (TP). The TP task was paired with an elemental control task, which does not depend upon the hippocampus. We contrasted two hypotheses; the compensation hypothesis would suggest that more bilateral activation in children would lead to better TP performance, whereas the maturation hypothesis would predict that a more adult-like right-lateralized pattern of hippocampal activation would lead to better performance. Mean-centered partial least squares analysis was used to determine unique patterns of brain activation specific to each task per group, while diminishing activation that is consistent across tasks. Our findings support the maturation hypothesis that a more adult-like pattern of increased right hippocampal lateralization in children leads to superior performance on the TP task. We also found dynamic changes of lateralization throughout the time course for all three groups, suggesting that caution is needed when interpreting conclusions about brain lateralization.