Stimulation of the Presupplementary Motor Area Cluster of the Subthalamic Nucleus Predicts More Consistent Clinical Outcomes.

BACKGROUND: The development of diffusion tensor imaging and tractography has raised increasing interest in the functional targeting of deep brain stimulation of the subthalamic nucleus (STN) in Parkinson disease. OBJECTIVE: To study, using deterministic tractography, the functional subdivisions of the STN and hyperdirect white matter connections located between the STN and the medial frontal cortex, especially the presupplementary motor area (preSMA), SMA, primary motor area (M1), and dorsolateral premotor cortex, and to study retrospectively whether this information correlates with clinical outcome. METHODS: Twenty-two patients with Parkinson disease who underwent STN deep brain stimulation were analyzed. Using 3 T MR images, the medial frontal cortex was manually segmented into preSMA, SMA, M1, and dorsolateral premotor cortex, which were then used to determine the functional subdivisions of the lateral border of the STN. The intersectional quantities of the volume of activated tissue (VAT) and the hyperdirect white matter connections were calculated. The results were combined with clinical data including unilateral 12-month postoperative motor outcome and levodopa equivalent daily dose. RESULTS: Stimulated clusters of the STN were connected mostly to the cortical SMA and preSMA regions. Patients with primarily preSMA cluster stimulation (presmaVAT% ≥ 50%) had good responses to the treatment with unilateral motor improvement over 40% and levodopa equivalent daily dose reduction over 60%. Larger VAT was not found to correlate with better patient outcomes. CONCLUSION: Our study is the first to suggest that stimulating, predominantly, the STN cluster where preSMA hyperdirect pathways are located, could be predictive of more consistent treatment results.

Intracranial bullet removal using O-arm navigation guidance.

PURPOSE: The purpose of this study is to report a new mini-invasive technique to remove an intracranial bullet in a child by using O-arm for intraoperative neuronavigation. CASE REPORT: A 14-year-old refugee boy had suffered a shooting injury 4 years earlier. O-arm imaging-assisted neuronavigation during craniotomy was performed in order to remove a bullet from the intracranial space in a paediatric patient. CONCLUSION: Navigation using O-arm is a feasible method in removing a foreign material in a child and gave an accurate location of the bullet in the adopted surgical operation position without significant imaging artefacts.

Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network.

Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting-state functional connectivity is temporally variable in human brain. Combined full-band electroencephalography-fMRI (fbEEG-fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood-oxygen-level-dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG-fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting-state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short-time-window comparisons of infra-slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG-BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG-fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG-BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on-average low correlations between infra-slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.

Real-time monitoring of human blood-brain barrier disruption.

Chemotherapy aided by opening of the blood-brain barrier with intra-arterial infusion of hyperosmolar mannitol improves the outcome in primary central nervous system lymphoma. Proper opening of the blood-brain barrier is crucial for the treatment, yet there are no means available for its real-time monitoring. The intact blood-brain barrier maintains a mV-level electrical potential difference between blood and brain tissue, giving rise to a measurable electrical signal at the scalp. Therefore, we used direct-current electroencephalography (DC-EEG) to characterize the spatiotemporal behavior of scalp-recorded slow electrical signals during blood-brain barrier opening. Nine anesthetized patients receiving chemotherapy were monitored continuously during 47 blood-brain barrier openings induced by carotid or vertebral artery mannitol infusion. Left or right carotid artery mannitol infusion generated a strongly lateralized DC-EEG response that began with a 2 min negative shift of up to 2000 µV followed by a positive shift lasting up to 20 min above the infused carotid artery territory, whereas contralateral responses were of opposite polarity. Vertebral artery mannitol infusion gave rise to a minimally lateralized and more uniformly distributed slow negative response with a posterior-frontal gradient. Simultaneously performed near-infrared spectroscopy detected a multiphasic response beginning with mannitol-bolus induced dilution of blood and ending in a prolonged increase in the oxy/deoxyhemoglobin ratio. The pronounced DC-EEG shifts are readily accounted for by opening and sealing of the blood-brain barrier. These data show that DC-EEG is a promising real-time monitoring tool for blood-brain barrier disruption augmented drug delivery.

Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data.

This study investigated lag structure in the resting-state fMRI by applying a novel independent component (ICA) method to magnetic resonance encephalography (MREG) data. Briefly, the spatial ICA (sICA) was used for defining the frontal and back nodes of the default mode network (DMN), and the temporal ICA (tICA), which is enabled by the high temporal resolution of MREG (TR=100ms), was used to separate both neuronal and physiological components of these two spatial map regions. Subsequently, lag structure was investigated between the frontal (DMNvmpf) and posterior (DMNpcc) DMN nodes using both conventional method with all-time points and a sliding-window approach. A rigorous noise exclusion criterion was applied for tICs to remove physiological pulsations, motion and system artefacts. All the de-noised tICs were used to calculate the null-distributions both for expected lag variability over time and over subjects. Lag analysis was done for the three highest correlating denoised tICA pairs. Mean time lag of 0.6s (± 0.5 std) and mean absolute correlation of 0.69 (± 0.08) between the highest correlating tICA pairs of DMN nodes was observed throughout the whole analyzed period. In dynamic 2min window analysis, there was large variability over subjects as ranging between 1-10sec. Directionality varied between these highly correlating sources an average 28.8% of the possible number of direction changes. The null models show highly consistent correlation and lag structure between DMN nodes both in continuous and dynamic analysis. The mean time lag of a null-model over time between all denoised DMN nodes was 0.0s and, thus the probability of having either DMNpcc or DMNvmpf as a preceding component is near equal. All the lag values of highest correlating tICA pairs over subjects lie within the standard deviation range of a null-model in whole time window analysis, supporting the earlier findings that there is a consistent temporal lag structure across groups of individuals. However, in dynamic analysis, there are lag values exceeding the threshold of significance of a null-model meaning that there might be biologically meaningful variation in this measure. Taken together the variability in lag and the presence of high activity peaks during strong connectivity indicate that individual avalanches may play an important role in defining dynamic independence in resting state connectivity within networks.

Ultra-fast magnetic resonance encephalography of physiological brain activity - Glymphatic pulsation mechanisms?

The theory on the glymphatic convection mechanism of cerebrospinal fluid holds that cardiac pulsations in part pump cerebrospinal fluid from the peri-arterial spaces through the extracellular tissue into the peri-venous spaces facilitated by aquaporin water channels. Since cardiac pulses cannot be the sole mechanism of glymphatic propulsion, we searched for additional cerebrospinal fluid pulsations in the human brain with ultra-fast magnetic resonance encephalography. We detected three types of physiological mechanisms affecting cerebral cerebrospinal fluid pulsations: cardiac, respiratory, and very low frequency pulsations. The cardiac pulsations induce a negative magnetic resonance encephalography signal change in peri-arterial regions that extends centrifugally and covers the brain in ≈1 Hz cycles. The respiratory ≈0.3 Hz pulsations are centripetal periodical pulses that occur dominantly in peri-venous areas. The third type of pulsation was very low frequency (VLF 0.001-0.023 Hz) and low frequency (LF 0.023-0.73 Hz) waves that both propagate with unique spatiotemporal patterns. Our findings using critically sampled magnetic resonance encephalography open a new view into cerebral fluid dynamics. Since glymphatic system failure may precede protein accumulations in diseases such as Alzheimer's dementia, this methodological advance offers a novel approach to image brain fluid dynamics that potentially can enable early detection and intervention in neurodegenerative diseases.