Neuroanatomical correlates of distracted straight driving performance: a driving simulator MRI study across the lifespan.

Background: Driving is the preferred mode of transportation for adults across the healthy age span. However, motor vehicle crashes are among the leading causes of injury and death, especially for older adults, and under distracted driving conditions. Understanding the neuroanatomical basis of driving may inform interventions that minimize crashes. This exploratory study examined the neuroanatomical correlates of undistracted and distracted simulated straight driving. Methods: One-hundred-and-thirty-eight participants (40.6% female) aged 17-85 years old (mean and SD = 58.1 ± 19.9 years) performed a simulated driving task involving straight driving and turns at intersections in a city environment using a steering wheel and foot pedals. During some straight driving segments, participants responded to auditory questions to simulate distracted driving. Anatomical T1-weighted MRI was used to quantify grey matter volume and cortical thickness for five brain regions: the middle frontal gyrus (MFG), precentral gyrus (PG), superior temporal cortex (STC), posterior parietal cortex (PPC), and cerebellum. Partial correlations controlling for age and sex were used to explore relationships between neuroanatomical measures and straight driving behavior, including speed, acceleration, lane position, heading angle, and time speeding or off-center. Effects of interest were noted at an unadjusted p-value threshold of 0.05. Results: Distracted driving was associated with changes in most measures of straight driving performance. Greater volume and cortical thickness in the PPC and cerebellum were associated with reduced variability in lane position and heading angle during distracted straight driving. Cortical thickness of the MFG, PG, PPC, and STC were associated with speed and acceleration, often in an age-dependent manner. Conclusion: Posterior regions were correlated with lane maintenance whereas anterior and posterior regions were correlated with speed and acceleration, especially during distracted driving. The regions involved and their role in straight driving may change with age, particularly during distracted driving as observed in older adults. Further studies should investigate the relationship between distracted driving and the aging brain to inform driving interventions.

History of traumatic brain injury is associated with increased grey-matter loss in patients with mild cognitive impairment.

OBJECTIVES: To investigate whether a history of traumatic brain injury (TBI) is associated with greater long-term grey-matter loss in patients with mild cognitive impairment (MCI). METHODS: 85 patients with MCI were identified, including 26 with a previous history of traumatic brain injury (MCI[TBI-]) and 59 without (MCI[TBI+]). Cortical thickness was evaluated by segmenting T1-weighted MRI scans acquired longitudinally over a 2-year period. Bayesian multilevel modelling was used to evaluate group differences in baseline cortical thickness and longitudinal change, as well as group differences in neuropsychological measures of executive function. RESULTS: At baseline, the MCI[TBI+] group had less grey matter within right entorhinal, left medial orbitofrontal and inferior temporal cortex areas bilaterally. Longitudinally, the MCI[TBI+] group also exhibited greater longitudinal declines in left rostral middle frontal, the left caudal middle frontal and left lateral orbitofrontal areas sover the span of 2 years (median = 1-2%, 90%HDI [-0.01%: -0.001%], probability of direction (PD) = 90-99%). The MCI[TBI+] group also displayed greater longitudinal declines in Trail-Making-Test (TMT)-derived ratio (median: 0.737%, 90%HDI: [0.229%: 1.31%], PD = 98.8%) and differences scores (median: 20.6%, 90%HDI: [-5.17%: 43.2%], PD = 91.7%). CONCLUSIONS: Our findings support the notion that patients with MCI and a history of TBI are at risk of accelerated neurodegeneration, displaying greatest evidence for cortical atrophy within the left middle frontal and lateral orbitofrontal frontal cortex. Importantly, these results suggest that long-term TBI-mediated atrophy is more pronounced in areas vulnerable to TBI-related mechanical injury, highlighting their potential relevance for diagnostic forms of intervention in TBI.

Persistent fatigue in post-acute COVID syndrome is associated with altered T1 MRI texture in subcortical structures: a preliminary investigation.

Post-acute COVID syndrome (PACS) is a global health concern and is often associated with debilitating symptoms. Post-COVID fatigue is a particularly frequent and troubling issue, and its underlying mechanisms remain incompletely understood. One potential contributor is micropathological injury of subcortical and brainstem structures, as has been identified in other patient populations. Texture-based analysis (TA) may be used to measure such changes in anatomical MRI data. The present study develops a methodology of voxel-wise TA mapping in subcortical and brainstem regions, which is then applied to T1-weighted MRI data from a cohort of 48 individuals who had PACS (32 with and 16 without ongoing fatigue symptoms) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19. Both groups were assessed an average of 4-5 months post-infection. There were no significant differences between PACS and control groups, but significant differences were observed within the PACS groups, between those with and without fatigue symptoms. This included reduced texture energy and increased entropy, along with reduced texture correlation, cluster shade and profile in the putamen, pallidum, thalamus and brainstem. These findings provide new insights into the neurophysiological mechanisms that underlie PACS, with altered tissue texture as a potential biomarker of this debilitating condition.

Task Activation Results in Regional 13 C-Lactate Signal Increase in the Human Brain.

Hyperpolarized- 13 C magnetic resonance imaging (HP- 13 C MRI) was used to image changes in 13 C-lactate signal during a visual stimulus condition in comparison to an eyes-closed control condition. Whole-brain 13 C-pyruvate, 13 C-lactate and 13 C-bicarbonate production was imaged in healthy volunteers (N=6, ages 24-33) for the two conditions using two separate hyperpolarized 13 C-pyruvate injections. BOLD-fMRI scans were used to delineate regions of functional activation. 13 C-metabolite signal was normalized by 13 C-metabolite signal from the brainstem and the percentage change in 13 C-metabolite signal conditions was calculated. A one-way Wilcoxon signed-rank test showed a significant increase in 13 C-lactate in regions of activation when compared to the remainder of the brain ( p = 0.02, V = 21). No significant increase was observed in 13 C-pyruvate ( p = 0.11, V = 17) or 13 C-bicarbonate ( p = 0.95, V = 3) signal. The results show an increase in 13 C-lactate production in the activated region that is measurable with HP- 13 C MRI.

Interaction of Alzheimer Disease and Traumatic Brain Injury on Cortical Thickness.

INTRODUCTION: Traumatic brain injury (TBI) is associated with an accelerated course of dementia, although biological relationships are incompletely understood. METHODS: The study examined 1124 participants, including 343 with Alzheimer disease (AD), 127 with AD with TBI, 266 cognitively normal adults with TBI, and 388 cognitively normal adults without TBI. Cortical thickness was quantified from T1-weighted magnetic resonance imaging data. Multiple linear regression was used to determine the interaction between AD and TBI on cortical thickness. RESULTS: Among those with AD, TBI was associated with an earlier age of AD onset but, counterintuitively, less cortical thinning in frontotemporal regions relative to non-AD controls. DISCUSSION: AD with TBI represents a distinct group from AD, likely with distinct pathologic contributions beyond gray matter loss. This finding has important implications for the diagnosis and treatment of AD in the presence of TBI and indicates that models of AD, aging, and neural loss should account for TBI history.

Mismatch between clinically defined classification of ALS stage and the burden of cerebral pathology.

This study aimed to investigate the clinical stratification of amyotrophic lateral sclerosis (ALS) patients in relation to in vivo cerebral degeneration. One hundred forty-nine ALS patients and one hundred forty-four healthy controls (HCs) were recruited from the Canadian ALS Neuroimaging Consortium (CALSNIC). Texture analysis was performed on T1-weighted scans to extract the texture feature "autocorrelation" (autoc), an imaging biomarker of cerebral degeneration. Patients were stratified at baseline into early and advanced disease stages based on criteria adapted from ALS clinical trials and the King's College staging system, as well as into slow and fast progressors (disease progression rates, DPR). Patients had increased autoc in the internal capsule. These changes extended beyond the internal capsule in early-stage patients (clinical trial-based criteria), fast progressors, and in advanced-stage patients (King's staging criteria). Longitudinal increases in autoc were observed in the postcentral gyrus, corticospinal tract, posterior cingulate cortex, and putamen; whereas decreases were observed in corpus callosum, caudate, central opercular cortex, and frontotemporal areas. Both longitudinal increases and decreases of autoc were observed in non-overlapping regions within insula and precentral gyrus. Within-criteria comparisons of autoc revealed more pronounced changes at baseline and longitudinally in early- (clinical trial-based criteria) and advanced-stage (King's staging criteria) patients and fast progressors. In summary, comparative patterns of baseline and longitudinal progression in cerebral degeneration are dependent on sub-group selection criteria, with clinical trial-based stratification insufficiently characterizing disease stage based on pathological cerebral burden.

Lateralized Functional Connectivity of the Sensorimotor Cortex and its Variations During Complex Visuomotor Tasks.

Previous studies have shown that the left hemisphere dominates motor function, often observed through homotopic activation measurements. Using a functional connectivity approach, this study investigated the lateralization of the sensorimotor cortex during handwriting and drawing, two complex visuomotor tasks with varying contextual demands. We found that both left- and right-lateralized connectivity in the primary motor cortex (M1), dorsal premotor cortex (PMd), somatosensory cortex, and visual regions were evident in adults (males and females), primarily in an interhemispheric integrative fashion. Critically, these lateralization tendencies remained highly invariant across task contexts, representing a task-invariant neural architecture for encoding fundamental motor programs consistently implemented in different task contexts. Additionally, the PMd exhibited a slight variation in lateralization degree between task contexts, reflecting the ability of the high-order motor system to adapt to varying task demands. However, connectivity-based lateralization of the sensorimotor cortex was not detected in 10-year-old children (males and females), suggesting that the maturation of connectivity-based lateralization requires prolonged development. In summary, this study demonstrates both task-invariant and task-sensitive connectivity lateralization in sensorimotor cortices that support the resilience and adaptability of skilled visuomotor performance. These findings align with the hierarchical organization of the motor system and underscore the significance of the functional connectivity-based approach in studying functional lateralization.

Persistent post-COVID headache is associated with suppression of scale-free functional brain dynamics in non-hospitalized individuals.

INTRODUCTION: Post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) is a growing concern, with headache being a particularly debilitating symptom with high prevalence. The long-term effects of COVID-19 and post-COVID headache on brain function remain poorly understood, particularly among non-hospitalized individuals. This study focused on the power-law scaling behavior of functional brain dynamics, indexed by the Hurst exponent (H). This measure is suppressed during physiological and psychological distress and was thus hypothesized to be reduced in individuals with post-COVID syndrome, with greatest reductions among those with persistent headache. METHODS: Resting-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging data were collected for 57 individuals who had COVID-19 (32 with no headache, 14 with ongoing headache, 11 recovered) and 17 controls who had cold and flu-like symptoms but  tested negative for COVID-19. Individuals were assessed an average of 4-5 months after COVID testing, in a cross-sectional, observational study design. RESULTS: No significant differences in H values were found between non-headache COVID-19 and control groups., while those with ongoing headache had significantly reduced H values, and those who had recovered from headache had elevated H values, relative to non-headache groups. Effects were greatest in temporal, sensorimotor, and insular brain regions. Reduced H in these regions was also associated with decreased BOLD activity and local functional connectivity. CONCLUSIONS: These findings provide new insights into the neurophysiological mechanisms that underlie persistent post-COVID headache, with reduced BOLD scaling as a potential biomarker that is specific to this debilitating condition.

SF2Former: Amyotrophic lateral sclerosis identification from multi-center MRI data using spatial and frequency fusion transformer.

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder characterized by motor neuron degeneration. Significant research has begun to establish brain magnetic resonance imaging (MRI) as a potential biomarker to diagnose and monitor the state of the disease. Deep learning has emerged as a prominent class of machine learning algorithms in computer vision and has shown successful applications in various medical image analysis tasks. However, deep learning methods applied to neuroimaging have not achieved superior performance in classifying ALS patients from healthy controls due to insignificant structural changes correlated with pathological features. Thus, a critical challenge in deep models is to identify discriminative features from limited training data. To address this challenge, this study introduces a framework called SF2Former, which leverages the power of the vision transformer architecture to distinguish ALS subjects from the control group by exploiting the long-range relationships among image features. Additionally, spatial and frequency domain information is combined to enhance the network's performance, as MRI scans are initially captured in the frequency domain and then converted to the spatial domain. The proposed framework is trained using a series of consecutive coronal slices and utilizes pre-trained weights from ImageNet through transfer learning. Finally, a majority voting scheme is employed on the coronal slices of each subject to generate the final classification decision. The proposed architecture is extensively evaluated with multi-modal neuroimaging data (i.e., T1-weighted, R2*, FLAIR) using two well-organized versions of the Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The experimental results demonstrate the superiority of the proposed strategy in terms of classification accuracy compared to several popular deep learning-based techniques.

Classifying youth with bipolar disorder versus healthy youth using cerebral blood flow patterns.

BACKGROUND: Clinical neuroimaging studies often investigate group differences between patients and controls, yet multivariate imaging features may enable individual-level classification. This study aims to classify youth with bipolar disorder (BD) versus healthy youth using grey matter cerebral blood flow (CBF) data analyzed with logistic regressions. METHODS: Using a 3 Tesla magnetic resonance imaging (MRI) system, we collected pseudo-continuous, arterial spin-labelling, resting-state functional MRI (rfMRI) and T 1-weighted images from youth with BD and healthy controls. We used 3 logistic regression models to classify youth with BD versus controls, controlling for age and sex, using mean grey matter CBF as a single explanatory variable, quantitative CBF features based on principal component analysis (PCA) or relative (intensity-normalized) CBF features based on PCA. We also carried out a comparison analysis using rfMRI data. RESULTS: The study included 46 patients with BD (mean age 17 yr, standard deviation [SD] 1 yr; 25 females) and 49 healthy controls (mean age 16 yr, SD 2 yr; 24 females). Global mean CBF and multivariate quantitative CBF offered similar classification performance that was above chance. The association between CBF images and the feature map was not significantly different between groups (p = 0.13); however, the multivariate classifier identified regions with lower CBF among patients with BD (ΔCBF = -2.94 mL/100 g/min; permutation test p = 0047). Classification performance decreased when considering rfMRI data. LIMITATIONS: We cannot comment on which CBF principal component is most relevant to the classification. Participants may have had various mood states, comorbidities, demographics and medication records. CONCLUSION: Brain CBF features can classify youth with BD versus healthy controls with above-chance accuracy using logistic regression. A global CBF feature may offer similar classification performance to distinct multivariate CBF features.

Technical note: System uncertainty on four- and eight-channel parallel RF transmission for safe MRI of deep brain stimulation devices.

BACKGROUND: Parallel radiofrequency transmission (pTx) remains a promising technology for addressing high-field magnetic resonance imaging (MRI) challenges, particularly regarding the safety of patients with implanted deep brain stimulation (DBS) devices. Radiofrequency (RF) shim optimization methods utilizing pTx technology have shown the potential to minimize induced RF heating effects at the electrode tips of DBS devices at 3 T. PURPOSE: Research pTx system implementations often involve the combination of custom and commercial hardware that are integrated onto an existing MRI system. As a result, system characterization is important to ensure implant-friendly safe imaging conditions are satisfied for the operating range of the hardware. METHODS: Utilizing electromagnetic and thermal simulations, the impact of system uncertainty is studied for the proposed 4- and 8-channel pTx system setup and its associated "safe mode" for DBS applications. RESULTS: Electromagnetic simulations indicated that instrumentation errors can affect the overall electric field strength experienced at the DBS lead tip, and a worst-case system uncertainty analysis predicted temperature elevations of +1.5°C in the 4-channel setup and +0.9°C in the 8-channel setup. CONCLUSIONS: In conclusion, system uncertainty can impact the precision of pTx RF inputs which in the worst-case, may lead to an unsafe imaging scenario and the proposed 8-channel setup may provide more robustness and thus, safer conditions for MRI of DBS patients.

Feasibility of diffusion-tensor and correlated diffusion imaging for studying white-matter microstructural abnormalities: Application in COVID-19.

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at the study initiation and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a viable single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

Effects of post-acute COVID-19 syndrome on the functional brain networks of non-hospitalized individuals.

Introduction: The long-term impact of COVID-19 on brain function remains poorly understood, despite growing concern surrounding post-acute COVID-19 syndrome (PACS). The goal of this cross-sectional, observational study was to determine whether there are significant alterations in resting brain function among non-hospitalized individuals with PACS, compared to symptomatic individuals with non-COVID infection. Methods: Data were collected for 51 individuals who tested positive for COVID-19 (mean age 41±12 yrs., 34 female) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19 (mean age 41±14 yrs., 9 female), with both groups assessed an average of 4-5 months after COVID testing. None of the participants had prior neurologic, psychiatric, or cardiovascular illness. Resting brain function was assessed via functional magnetic resonance imaging (fMRI), and self-reported symptoms were recorded. Results: Individuals with COVID-19 had lower temporal and subcortical functional connectivity relative to controls. A greater number of ongoing post-COVID symptoms was also associated with altered functional connectivity between temporal, parietal, occipital and subcortical regions. Discussion: These results provide preliminary evidence that patterns of functional connectivity distinguish PACS from non-COVID infection and correlate with the severity of clinical outcome, providing novel insights into this highly prevalent disorder.

Perfusion Imaging of Traumatic Brain Injury.

The mechanisms for regulating cerebral blood flow (CBF) are highly sensitive to traumatic brain injury (TBI). The perfusion imaging technique may be used to assess CBF and identify perfusion abnormalities following a TBI. Studies have identified CBF disturbances across the injury severity spectrum and correlations with both acute and long-term indices of clinical outcome. Although not yet widely used in the clinical context, this is an important area of ongoing research.

Measuring Brain Temperature in Youth Bipolar Disorder Using a Novel Magnetic Resonance Imaging Approach: A Proof-of-concept Study.

BACKGROUND: There is evidence of alterations in mitochondrial energy metabolism and cerebral blood flow (CBF) in adults and youth with bipolar disorder (BD). Brain thermoregulation is based on the balance of heat-producing metabolism and heat-dissipating mechanisms, including CBF. OBJECTIVE: To examine brain temperature, and its relation to CBF, in relation to BD and mood symptom severity in youth. METHODS: This study included 25 youth participants (age 17.4 ± 1.7 years; 13 BD, 12 control group (CG)). Magnetic resonance spectroscopy data were acquired to obtain brain temperature in the left anterior cingulate cortex (ACC) and the left precuneus. Regional estimates of CBF were provided by arterial spin labeling imaging. Analyses used general linear regression models, covarying for age, sex, and psychiatric medications. RESULTS: Brain temperature was significantly higher in BD compared to CG in the precuneus. A higher ratio of brain temperature to CBF was significantly associated with greater depression symptom severity in both the ACC and precuneus within BD. Analyses examining the relationship of brain temperature or CBF with depression severity score did not reveal any significant finding in the ACC or the precuneus. CONCLUSION: The current study provides preliminary evidence of increased brain temperature in youth with BD, in whom reduced thermoregulatory capacity is putatively associated with depression symptom severity. Evaluation of brain temperature and CBF in conjunction may provide valuable insight beyond what can be gleaned by either metric alone. Larger prospective studies are warranted to further evaluate brain temperature and its association with CBF concerning BD.

Altered connectivity of default mode and executive control networks among female patients with persistent post-concussion symptoms.

OBJECTIVE: To examine the roles of the default mode network (DMN) and executive control network (ECN) in prolonged recovery after mild traumatic brain injury (mTBI), and relationships with indices of white matter microstructural injury. METHODS: Seventeen mTBI patients with persistent symptoms were imaged an average of 21.5 months post-injury, along with 23 healthy controls. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to evaluate functional connectivity (FC) of the DMN and ECN. Diffusion tensor imaging (DTI) quantified fractional anisotropy, along with mean, axial and radial diffusivity of white matter tracts. RESULTS: Compared to controls, patients with mTBI had increased functional connectivity of the DMN and ECN to brain regions implicated in salience and frontoparietal networks, and increased white matter diffusivity within the cerebrum and brainstem. Among the patients, FC was correlated with better neurocognitive test scores, while diffusivity was correlated with more severe self-reported symptoms. The FC and diffusivity values within abnormal brain regions were not significantly correlated. CONCLUSION: For female mTBI patients with prolonged symptoms, hyper-connectivity may represent a compensatory response that helps to mitigate the effects of mTBI on cognition. These effects are unrelated to indices of microstructural injury, which are correlated with symptom severity, suggesting that rs-fMRI and DTI may capture distinct aspects of pathophysiology.

MRI Assessment of Cerebral Blood Flow in Nonhospitalized Adults Who Self-Isolated Due to COVID-19.

BACKGROUND: Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe. PURPOSE: To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19. STUDY TYPE: Prospective observational. POPULATION: A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis). FIELD STRENGTH AND SEQUENCES: A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences. ASSESSMENT: Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n = 11) and without self-reported ongoing fatigue (n = 28) within the COVID-19 group. STATISTICAL TESTS: Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05. RESULTS: Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t-statistic = 5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue. DATA CONCLUSION: These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

Electroencephalography can provide advance warning of technical errors during laparoscopic surgery.

BACKGROUND: Intraoperative adverse events lead to patient injury and death, and are increasing. Early warning systems (EWSs) have been used to detect patient deterioration and save lives. However, few studies have used EWSs to monitor surgical performance and caution about imminent technical errors. Previous (non-surgical) research has investigated neural activity to predict future motor errors using electroencephalography (EEG). The present proof-of-concept cohort study investigates whether EEG could predict technical errors in surgery. METHODS: In a large academic hospital, three surgical fellows performed 12 elective laparoscopic general surgeries. Audiovisual data of the operating room and the surgeon's neural activity were recorded. Technical errors and epochs of good surgical performance were coded into events. Neural activity was observed 40 s prior and 10 s after errors and good events to determine how far in advance errors were detected. A hierarchical regression model was used to account for possible clustering within surgeons. This prospective, proof-of-concept, cohort study was conducted from July to November 2021, with a pilot period from February to March 2020 used to optimize the technique of data capture and included participants who were blinded from study hypotheses. RESULTS: Forty-five technical errors, mainly due to too little force or distance (n = 39), and 27 good surgical events were coded during grasping and dissection. Neural activity representing error monitoring (p = .008) and motor uncertainty (p = .034) was detected 17 s prior to errors, but not prior to good surgical performance. CONCLUSIONS: These results show that distinct neural signatures are predictive of technical error in laparoscopic surgery. If replicated with low false-alarm rates, an EEG-based EWS of technical errors could be used to improve individualized surgical training by flagging imminent unsafe actions-before errors occur and cause patient harm.

Deep brain stimulation of the nucleus accumbens in the treatment of severe alcohol use disorder: a phase I pilot trial.

Alcohol use disorder (AUD) is a highly prevalent, often refractory, medical illness. The symptoms of AUD are driven by dysfunction in several neurocircuits centered on the nucleus accumbens (NAc). Case reports and animal studies suggest NAc-DBS may be an effective harm-reduction treatment in severe AUD. Six patients with severe, refractory AUD underwent NAc-DBS. Safety metrics and clinical outcomes were recorded. Positron emission tomography (FDG-PET) was used to measure glucose metabolism in the NAc at baseline and 6 months. Functional magnetic resonance imaging (fMRI) was used to characterize postoperative changes in NAc functional connectivity to the rest of the brain, as well as NAc and dorsal striatal reactivity to alcoholic visual cues. This study was registered with ClinicalTrials.gov, NCT03660124. All patients experienced a reduction in craving. There was a significant reduction in alcohol consumption, alcohol-related compulsivity, and anxiety at 12 months. There was no significant change in depression. FDG-PET analysis demonstrated reduced NAc metabolism by 6 months, which correlated with improvements in compulsive drinking behaviors. Clinical improvement correlated with reduced functional connectivity between the NAc and the visual association cortex. Active DBS was associated with reduced activation of the dorsal striatum during passive viewing of alcohol-containing pictures. NAc-DBS is feasible and safe in patients with severe, otherwise refractory AUD. It is associated with a reduction in cravings and addictive behavior. A potential mechanism underlying this process is a down-regulation of the NAc, a disruption of its functional connectivity to the visual association cortex, and interference of cue-elicited dorsal striatum reactivity. Trial Registration NCT03660124 ( www.clinicaltrials.gov ).

Ballistocardiogram suppression in concurrent EEG-MRI by dynamic modeling of heartbeats.

The ballistocardiogram (BCG), the induced electric potentials by the head motion originating from heartbeats, is a prominent source of noise in electroencephalography (EEG) data during magnetic resonance imaging (MRI). Although methods have been proposed to suppress the BCG artifact, more work considering the variability of cardiac cycles and head motion across time and subjects is needed to provide highly robust correction. Here, a method called "dynamic modeling of heartbeats" (DMH) is proposed to reduce BCG artifacts in EEG data recorded inside an MRI system. The DMH method models BCG artifacts by combining EEG points at time instants with similar dynamics. The modeled BCG artifact is then subtracted from the EEG recording to suppress the BCG artifact. Performance of DMH was tested and specifically compared with the Optimal Basis Set (OBS) method on EEG data recorded inside a 3T MRI system with either no MRI acquisition (Inside-MRI), echo-planar imaging (EPI-EEG), or fast MRI acquisition using simultaneous multi-slice and inverse imaging methods (SMS-InI-EEG). In a steady-state visual evoked response (SSVEP) paradigm, the 15-Hz oscillatory neuronal activity at the visual cortex after DMH processing was about 130% of that achieved by OBS processing for Inside-MRI, SMS-InI-EEG, and EPI-EEG conditions. The DMH method is computationally efficient for suppressing BCG artifacts and in the future may help to improve the quality of EEG data recorded in high-field MRI systems for neuroscientific and clinical applications.