Brain Networks Research Articles

Understanding Anxiety in Cervical Dystonia: An Imaging Study.

Anxiety may precede motor symptoms in cervical dystonia (CD) and is associated with an earlier onset of dystonia. Our understanding of anxiety in CD is inadequate. To investigate brain networks associated with anxiety in CD. Twenty-six subjects with idiopathic CD underwent MRI Brain without contrast. Correlational tractography was derived using Diffusion MRI connectometry. Quantitative Anisotropy (QA) was used in deterministic diffusion fiber tracking. Correlational tractography was then used to correlate QA with State-Trait Anxiety Inventory (STAI) state (STAI-S) and trait (STAI-T) subscales. Connectometry analysis showed direct correlation between state anxiety and QA in tracts from amygdala to thalamus/ pulvinar bilaterally, and trait anxiety and QA in tracts from amygdala to motor cortex, sensorimotor cortex and parietal association area bilaterally (FDR ≤0.05). Our efforts to map anxiety to brain networks in CD highlight the role of the amygdala in the pathophysiology of anxiety in CD.

Functional alterations in overweight/obesity: focusing on the reward and executive control network.

Overweight (OW) and obesity (OB) have become prevalent issues in the global public health arena. Serving as a prominent risk factor for various chronic diseases, overweight/obesity not only poses serious threats to people's physical and mental health but also imposes significant medical and economic burdens on society as a whole. In recent years, there has been a growing focus on basic scientific research dedicated to seeking the neural evidence underlying overweight/obesity, aiming to elucidate its causes and effects by revealing functional alterations in brain networks. Among them, dysfunction in the reward network (RN) and executive control network (ECN) during both resting state and task conditions is considered pivotal in neuroscience research on overweight/obesity. Their aberrations contribute to explaining why persons with overweight/obesity exhibit heightened sensitivity to food rewards and eating disinhibition. This review centers on the reward and executive control network by analyzing and organizing the resting-state and task-based fMRI studies of functional brain network alterations in overweight/obesity. Building upon this foundation, the authors further summarize a reward-inhibition dual-system model, with a view to establishing a theoretical framework for future exploration in this field.

Alterations in brain network connectivity and subjective experience induced by psychedelics: a scoping review

Alterations in brain network connectivity and subjective experience induced by psychedelics: a scoping review

Topological state-space estimation of functional human brain networks.

We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice

Abstract Studies of intracranial EEG (iEEG) networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. iEEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal iEEG measures brain activity at baseline and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. iEEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal iEEG across two epilepsy centers to generate iEEG functional connectivity networks. 24 functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing (CAR) or bipolar re-referencing (BR). We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: 1) the introduction of spurious correlations, 2) robustness to incomplete spatial sampling, and 3) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: CAR pipelines, BR pipelines, and relative entropy-based connectivity pipelines. Relative entropy and CAR networks were more robust to incomplete electrode sampling than BR and other connectivity methods (Friedman test, Dunn-Sˇidák test p < 0.0001). BR reduced spurious correlations at non-adjacent channels better than CAR (Δ mean from machine ref = -0.36 vs -0.22), and worse in adjacent channels (Δ mean from machine ref = -0.14 vs -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (BH-corrected p < 0.05, Cohen’s d: 0.60 - 0.76). Finally, we present an interface where users can rapidly evaluate iEEG pre-processing choices to select the optimal preprocessing methods tailored to specific research questions. The choice of preprocessing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their preprocessing methods for deriving iEEG brain networks.

The cortical neurophysiological signature of amyotrophic lateral sclerosis

Abstract The progressive loss of motor function characteristic of amyotrophic lateral sclerosis is associated with widespread cortical pathology extending beyond primary motor regions. Increasing muscle weakness reflects a dynamic, variably compensated brain network disorder. In the quest for biomarkers to accelerate therapeutic assessment, the high temporal resolution of magnetoencephalography is uniquely able to non-invasively capture micro-magnetic fields generated by neuronal activity across the entire cortex simultaneously. This study examined task-free magnetoencephalography to characterise the cortical oscillatory signature of amyotrophic lateral sclerosis for potential as a pharmacodynamic biomarker. Eight-ten minutes of magnetoencephalography in the task-free, eyes-open state was recorded in amyotrophic lateral sclerosis (n=36) and healthy age-matched controls (n=51), followed by a structural MRI scan for co-registration. Extracted magnetoencephalography metrics from the delta, theta, alpha, beta, low-gamma, high-gamma frequency bands included oscillatory power (regional activity), 1/f exponent (complexity) and amplitude envelope correlation (connectivity). Groups were compared using a permutations-based general linear model with correction for multiple comparisons and confounders. To test whether the extracted metrics could predict disease severity, a Random Forest Regression model was trained and evaluated using nested Leave-One-Out Cross-Validation. Amyotrophic lateral sclerosis was characterised by reduced sensorimotor beta band and increased high-gamma band power. Within the premotor cortex, increased disability was associated with a reduced 1/f exponent. Increased disability was more widely associated with increased global connectivity in the delta, theta, and high-gamma bands. Intra-hemispherically, increased disability scores were particularly associated with increases in temporal connectivity and inter-hemispherically with increases in frontal and occipital connectivity. The Random Forest model achieved a coefficient of determination (R2) of 0.24. The combined reduction in cortical sensorimotor beta and rise in gamma power is compatible with the established hypothesis of loss of inhibitory, GABAergic interneuronal circuits in pathogenesis. A lower 1/f exponent potentially reflects a more excitable cortex and a pathology unique to amyotrophic lateral sclerosis when considered with the findings published in other neurodegenerative disorders. Power and complexity changes corroborate with the results from paired-pulse transcranial magnetic stimulation. Increased magnetoencephalography connectivity in worsening disability is thought to represent compensatory responses to a failing motor system. Restoration of cortical beta and gamma band power has significant potential to be tested in an experimental medicine setting. Magnetoencephalography-based measures have potential as sensitive outcome measures of therapeutic benefit in drug trials and may have wider diagnostic value with further study, including as predictive markers in asymptomatic carriers of disease-causing genetic variants.

Effects and mechanisms of computerized cognitive training in Huntington's disease: protocol for a pilot study

Huntington's disease (HD) causes progressive cognitive decline, with no available treatments. Computerized cognitive training (CCT) has shown efficacy in other populations, but its effects in HD are largely unknown. This pilot study will explore the effects and neural mechanisms of CCT in HD. The intervention group participants will complete 12 weeks of multidomain CCT. Control group participants will receive lifestyle education and access to CCT after the study. The primary outcome is change in processing speed. Secondary outcomes include – change in other cognitive domains, functional brain network connectivity (derived from MRI) and psychosocial function. Feasibility outcomes include rates of recruitment, adherence and retention. This study may provide insights into the effects of CCT in HD and guide future trials. Clinical Trial Registration: ACTRN12622000908730 ( ClinicalTrials.gov )

Graph Analysis of the Visual Cortical Network during Naturalistic Movie Viewing Reveals Increased Integration and Decreased Segregation Following Mild TBI

Traditional neuroimaging methods have identified alterations in brain activity patterns following mild traumatic brain injury (mTBI), particularly during rest, complex tasks, and normal vision. However, studies using graph theory to examine brain network changes in mTBI have produced varied results, influenced by the specific networks and task demands analyzed. In our study, we employed functional MRI to observe 17 mTBI patients and 54 healthy individuals as they viewed a simple, non-narrative underwater film, simulating everyday visual tasks. This approach revealed significant mTBI-related changes in network connectivity, efficiency, and organization. Specifically, the mTBI group exhibited higher overall connectivity and local network specialization, suggesting enhanced information integration without overwhelming the brain’s processing capabilities. Conversely, these patients showed reduced network segregation, indicating a less compartmentalized brain function compared to healthy controls. These patterns were consistent across various visual cortex subnetworks, except in primary visual areas. Our findings highlight the potential of using naturalistic stimuli in graph-based neuroimaging to understand brain network alterations in mTBI and possibly other conditions affecting brain integration.

The maintenance of complex visual scenes in working memory may require activation of working memory manipulation circuits in the dlPFC: A preliminary report

AbstractPast research has shown that the bilateral dorsolateral prefrontal cortices (dlPFC) are implicated in both emotional processing as well as cognitive processing,1,2,3 in addition to working memory4, 5. Exactly how these disparate processes interact with one another within the dlPFC is less understood. To explore this, we designed a task that looked at working memory performance during fMRI under both emotional and nonemotional conditions, and tested it in this preliminary report. Participants were asked to complete three tasks (letters, neutral images, emotional images) of the Sternberg Sorting Task under one of two trial conditions (sort or maintain). Regions of interest consisted of the left and right dlPFC as defined by brain masks based on NeuroSynth6. Results showed a significant main effect of the ‘sort’ condition on reaction speed for all three trial types, as well as a main effect of task type (letters) on accuracy. In addition, a significant interaction was found between trial type (sort) and task type (letters), but not for either of the picture tasks. Although preliminary, these results reveal a discrepancy between BOLD signal and behavioral data, with no significant difference in BOLD activity during image trials being displayed, despite longer response times for every condition. While these initial results show that the dlPFC is implicated in nonemotional cognitive processing, more research is needed to explain the lack of BOLD activation seen here for similar emotionally valanced tasks, possibly indicating the involvement of other brain networks.

Distinct roles of excitatory and inhibitory neurons in the medial prefrontal cortex in the expression and reconsolidation of methamphetamine-associated memory in male mice.

Methamphetamine, a commonly abused drug, is known for its high relapse rate. The persistence of addictive memories associated with methamphetamine poses a significant challenge in preventing relapse. Memory retrieval and subsequent reconsolidation provide an opportunity to disrupt addictive memories. However, the key node in the brain network involved in methamphetamine-associated memory retrieval has not been clearly defined. In this study, using the conditioned place preference in male mice, whole brain c-FOS mapping and functional connectivity analysis, together with chemogenetic manipulations of neural circuits, we identified the medial prefrontal cortex (mPFC) as a critical hub that integrates inputs from the retrosplenial cortex and the ventral tegmental area to support both the expression and reconsolidation of methamphetamine-associated memory during its retrieval. Surprisingly, with further cell-type specific analysis and manipulation, we also observed that methamphetamine-associated memory retrieval activated inhibitory neurons in the mPFC to facilitate memory reconsolidation, while suppressing excitatory neurons to aid memory expression. These findings provide novel insights into the neural circuits and cellular mechanisms involved in the retrieval process of addictive memories. They suggest that targeting the balance between excitation and inhibition in the mPFC during memory retrieval could be a promising treatment strategy to prevent relapse in methamphetamine addiction.

Educational Attainment Moderates Task-state Control Network Connectivity Relations to Response Conflict Among Healthy Older Adults.

Older adult executive function varies widely due to brain and cognitive aging. Variance in older adult executive function is linked to increased response conflict from cognitive and brain aging. Cognitive Reserve (CR) is a theoretical protective mechanism that lessens brain aging's impact on cognition and is associated with greater educational attainment. Recent work in rest-state fMRI suggests CR proxies moderate the relationship between functional connectivity (FC) and cognitive performance. Brain network FC in "control networks", including the salience (SN), dorsal attention (DAN) and frontoparietal (FPN) networks, are associated with cognitive processes in older adults. CR is hypothesized to maintain cognitive processing in part through changes in how brain networks respond to cognitive demands. However, it is unclear how CR proxies like educational attainment are related to control network FC during performance when cognitive demands are increased relative to rest. Because CR is expressed more in those with higher education, we hypothesized stronger control network FC would relate to better performance, where this relationship would be strongest among the most educated. We collected flanker task data during fMRI to assess the impact of a CR-proxy (i.e., educational attainment) on response conflict among older adult subjects (n=42, age=65-80). Linear mixed effects models showed more educated older adults with greater SN-FC had a smaller flanker effect (i.e., less influence of distractors; p<.001) during task performance. For the first time, we show that educational attainment moderates the relationship between task-state SN-FC and executive function among older adults.

Long-Term Implicit Epigenetic Stress Information in the Enteric Nervous System and its Contribution to Developing and Perpetuating IBS.

Psychiatric and mood disorders may play an important role in the development and persistence of irritable bowel syndrome (IBS). Previously, we hypothesized that stress-induced implicit memories may persist throughout life via epigenetic processes in the enteric nervous system (ENS), independent of the central nervous system (CNS). These epigenetic memories in the ENS may contribute to developing and perpetuating IBS. Here, we further elaborate on our earlier hypothesis. That is, during pregnancy, maternal prenatal stresses perturb the HPA axis and increase circulating cortisol levels, which can affect the maternal gut microbiota. Maternal cortisol can cross the placental barrier and increase cortisol-circulating levels in the fetus. This leads to dysregulation of the HPA axis, affecting the gut microbiota, microbial metabolites, and intestinal permeability in the fetus. Microbial metabolites, such as short-chain fatty acids (which also regulate the development of fetal ENS), can modulate a range of diseases by inducing epigenetic changes. These mentioned processes suggest that stress-related, implicit, long-term epigenetic memories may be programmed into the fetal ENS during pregnancy. Subsequently, this implicit epigenetic stress information from the fetal ENS could be conveyed to the CNS through the bidirectional microbiota-gut-brain axis (MGBA), leading to perturbed functional connectivity among various brain networks and the dysregulation of affective and pain processes.

Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit

Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatment, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.

Reduced lateralization of multiple functional brain networks in autistic males

BackgroundAutism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly.Methods In the present study, we estimated brain hemispheric lateralization in autism based on each participant’s unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups.ResultsWe found significant group differences in lateralization for the left-lateralized Language (d = -0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic males. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic males with language delay and neurotypical individuals.ConclusionsThese findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Moreover, we observed an association between Language network lateralization and language delay in autistic males.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Targeting thalamocortical circuits for closed-loop stimulation in Lennox-Gastaut syndrome

Abstract This paper outlines the therapeutic rationale and neurosurgical targeting technique for bilateral, closed-loop, thalamocortical stimulation in Lennox-Gastaut syndrome, a severe form of childhood-onset epilepsy. Thalamic stimulation can be an effective treatment for Lennox-Gastaut syndrome, but complete seizure control is rarely achieved. Outcomes may be improved by stimulating areas beyond the thalamus, including cortex, but the optimal targets are unknown. We aimed to identify a cortical target by synthesizing prior neuroimaging studies, and to use this knowledge to advance a dual thalamic (centromedian) and cortical (frontal) approach for closed-loop stimulation. Multi-modal brain network maps from 3 group-level studies of Lennox-Gastaut syndrome were averaged to define the area of peak overlap: simultaneous EEG-functional MRI of generalized paroxysmal fast activity, [18F]fluorodeoxyglucose PET of cortical hypometabolism, and diffusion MRI structural connectivity associated with clinical efficacy in a previous trial of thalamic deep brain stimulation. The resulting “hotspot” was used as a seed in a normative functional MRI connectivity analysis to identify connected networks. Intracranial electrophysiology was reviewed in the first two trial patients undergoing bilateral implantations guided by this hotspot. Simultaneous recordings from cortex and thalamus were analyzed for presence and synchrony of epileptiform activity. The peak overlap was in bilateral premotor cortex/caudal middle frontal gyrus. Functional connectivity of this hotspot revealed a distributed network of frontoparietal cortex resembling the diffuse abnormalities seen on EEG-functional MRI and PET. Intracranial electrophysiology showed characteristic epileptiform activity of Lennox-Gastaut syndrome in both the cortical hotspot and thalamus; most detected events occurred first in the cortex before appearing in the thalamus. Premotor frontal cortex shows peak involvement in Lennox-Gastaut syndrome and functional connectivity of this region resembles the wider epileptic brain network. Thus, it may be an optimal target for a range of neuromodulation therapies, including thalamocortical stimulation and emerging non-invasive treatments like focused ultrasound or transcranial magnetic stimulation. Compared to thalamus-only approaches, the addition of this cortical target may allow more rapid detections of seizures, more diverse stimulation paradigms, and broader modulation of the epileptic network. A prospective, multi-center trial of closed-loop thalamocortical stimulation for Lennox-Gastaut syndrome is currently underway.

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation

BackgroundVirtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue. ObjectiveIn this paper, a convolutional neural network (CNN) EEG detection model based on multi-scale feature correlation is proposed for detecting VRMS. MethodsThe model uses multi-scale 1D convolutional layers to extract multi-scale temporal features from the multi-lead EEG data, and then calculates the feature correlations of the extracted multi-scale features among all the leads to form the feature adjacent matrixes, which converts the time-domain features to correlation-based brain network features, thus strengthen the feature representation. Finally, the correlation features of each layer are fused. The fused features are then fed into the channel attention module to filter the channels and classify them using a fully connected network. Finally, we recruit subjects to experience 6 different modes of virtual roller coaster scenes, and collect resting EEG data before and after the task to verify the model. Results: The results show that the accuracy, precision, recall and F1-score of this model for the recognition of VRMS are 98.66 %, 98.65 %, 98.68 %, and 98.66 %, respectively. The proposed model outperforms the current classic and advanced EEG recognition models. SignificanceIt shows that this model can be used for the recognition of VRMS based on the resting state EEG.

The dynamics of functional brain network segregation in feedback-driven learning

Prior evidence suggests that increasingly efficient task performance in human learning is associated with large scale brain network dynamics. However, the specific nature of this general relationship has remained unclear. Here, we characterize performance improvement during feedback-driven stimulus-response (S-R) learning by learning rate as well as S-R habit strength and test whether and how these two behavioral measures are associated with a functional brain state transition from a more integrated to a more segregated brain state across learning. Capitalizing on two separate fMRI studies using similar but not identical experimental designs, we demonstrate for both studies that a higher learning rate is associated with a more rapid brain network segregation. By contrast, S-R habit strength is not reliably related to changes in brain network segregation. Overall, our current study results highlight the utility of dynamic functional brain state analysis. From a broader perspective taking into account previous study results, our findings align with a framework that conceptualizes brain network segregation as a general feature of processing efficiency not only in feedback-driven learning as in the present study but also in other types of learning and in other task domains.

Illusions of Self-Motion during Magnetic Resonance-Guided Focused Ultrasound Thalamotomy for Tremor.

Brain networks mediating vestibular perception of self-motion overlap with those mediating balance. A systematic mapping of vestibular perceptual pathways in the thalamus may reveal new brain modulation targets for improving balance in neurological conditions. Here, we systematically report how magnetic resonance-guided focused ultrasound surgery of the nucleus ventralis intermedius of the thalamus commonly evokes transient patient-reported illusions of self-motion. In 46 consecutive patients, we linked the descriptions of self-motion to sonication power and 3-dimensional (3D) coordinates of sonication targets. Target coordinates were normalized using a standard atlas, and a 3D model of the nucleus ventralis intermedius and adjacent structures was created to link sonication target to the illusion. A total of 63% of patients reported illusions of self-motion, which were more likely with increased sonication power and with targets located more inferiorly along the rostrocaudal axis. Higher power and more inferiorly targeted sonications increased the likelihood of experiencing illusions of self-motion by 4 and 2 times, respectively (odds ratios = 4.03 for power, 2.098 for location). The phenomenon of magnetic vestibular stimulation is the most plausible explanation for these illusions of self-motion. Temporary unilateral modulation of vestibular pathways (via magnetic resonance-guided focused ultrasound) unveils the central adaptation to the magnetic field-induced peripheral vestibular bias, leading to an explicable illusion of motion. Consequently, systematic mapping of vestibular perceptual pathways via magnetic resonance-guided focused ultrasound may reveal new intracerebral targets for improving balance in neurological conditions. ANN NEUROL 2024.

Functional brain network analysis using electroencephalography in late-onset Lennox–Gastaut syndrome

ObjectiveWe aimed to explore the clinical characteristics and functional network properties of patients with late-onset Lennox–Gastaut syndrome (LGS). MethodsLate-onset LGS was defined by the appearance of LGS features after 8 years of age. We reviewed the medical charts of 9 patients with late-onset LGS, and performed electroencephalography connectivity analysis using graph theory. We assessed the clustering coefficient (CC) and characteristic path length (CPL), which are common basic measures of functional networks that represent local segregation and global integration. The characteristics and brain parameters of late-onset LGS were compared with a typical age-onset LGS group. ResultsLate onset LGS subjects were older than typical age onset LGS at the time of testing, but otherwise there were no significant differences in clinical characteristics. The late-onset group showed higher median CC values in the alpha (p = 0.045) and beta (p < 0.001) bands over brain regions implicated in cognitive processing. There were no significant differences in CPL between the LGS groups. ConclusionsHigher clustering coefficient values, in alpha/beta bands over brain regions implicated in cognitive processing, are consistent with increased cognitive network segregation in late onset LGS compared to typical age-onset LGS. Given network segregation is a normal aspect of brain maturation, these results imply that this process is less disturbed when the LGS process begins later in childhood.