Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex.

For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity-based parcellation approach that can be applied at the single-subject level. Utilizing normative diffusion data, we first developed a machine-learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients (n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient-specific maps independent of brain shape and pathological distortion. The supervised ML classifier re-parcellated an average of 2.65% of cortical voxels across a healthy dataset (n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re-parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas-based registration from healthy subjects by employing a voxel-wise connectivity approach based on individual data

Parcellation-Based Connectivity Model of the Judgement Core

Judgement is a higher-order brain function utilized in the evaluation process of problem solving. However, heterogeneity in the task methodology based on the many definitions of judgement and its expansive and nuanced applications have prevented the identification of a unified cortical model at a level of granularity necessary for clinical translation. Forty-six task-based fMRI studies were used to generate activation-likelihood estimations (ALE) across moral, social, risky, and interpersonal judgement paradigms. Cortical parcellations overlapping these ALEs were used to delineate patterns in neurocognitive network engagement for the four judgement tasks. Moral judgement involved the bilateral superior frontal gyri, right temporal gyri, and left parietal lobe. Social judgement demonstrated a left-dominant frontoparietal network with engagement of right-sided temporal limbic regions. Moral and social judgement tasks evoked mutual engagement of the bilateral DMN. Both interpersonal and risk judgement were shown to involve a right-sided frontoparietal network with accompanying engagement of the left insular cortex, converging at the right-sided CEN. Cortical activation in normophysiological judgement function followed two separable patterns involving the large-scale neurocognitive networks. Specifically, the DMN was found to subserve judgement centered around social inferences and moral cognition, while the CEN subserved tasks involving probabilistic reasoning, risk estimation, and strategic contemplation

Functional connectivity in ADHD children doing Go/No-Go tasks: An fMRI systematic review and meta-analysis

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: p < 0.001; cluster-level: p < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial

Functional connectivity of the language area in migraine: a preliminary classification model

Abstract Background Migraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and treatment. Methods Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI(rfMRI), and diffusion weighted scans were obtained from 31 patients with migraine, and 17 controls. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into diagnostic groups based on functional connectivity (FC) and derive networks and parcels contributing to the model. PageRank centrality analysis was also performed on the structural connectome to identify changes in hubness. Results Our model attained an area under the receiver operating characteristic curve (AUC-ROC) of 0.68, which rose to 0.86 following hyperparameter tuning. FC of the language network was most predictive of the model’s classification, though patients with migraine also demonstrated differences in the accessory language, visual and medial temporal regions. Several analogous regions in the right hemisphere demonstrated changes in PageRank centrality, suggesting possible compensation. Conclusions Although our small sample size demands caution, our preliminary findings demonstrate the utility of our method in providing a network-based perspective to diagnosis and treatment of migraine

Transesophageal echocardiography for cardiovascular risk estimation in patients with sepsis and new-onset atrial fibrillation: a multicenter prospective pilot study

International audienceBackground: Echocardiographic parameters have been poorly investigated for estimating cardiovascular risk in patients with sepsis and new-onset atrial fibrillation. We aim to assess the prevalence of transesophageal echocardiographic abnormalities and their relationship with cardiovascular events in mechanically ventilated patients with sepsis and new-onset atrial fibrillation. Methods: In this prospective multicenter pilot study, left atrial/left atrial appendage (LA/LAA) dysfunction, severe aortic atheroma, and left ventricular systolic dysfunction were assessed using an initial transesophageal echocardiographic study, which was repeated after 48-72 h to detect LA/LAA thrombus formation. The study outcome was a composite of cardiovascular events at day 28, including arterial thromboembolic events (ischemic stroke, non-cerebrovascular arterial thromboembolism, LA/LAA thrombus), major bleeding, and all-cause death. Results: The study population comprised 94 patients (septic shock 63%; 35% women; median age 69 years). LA/LAA dysfunction, severe aortic atheroma, and left ventricular systolic dysfunction were detected in 17 (19%), 22 (24%), and 27 (29%) patients, respectively. At day 28, the incidence of cardiovascular events was 46% (95% confidence interval [CI]: 35 to 56). Arterial thromboembolic events and major bleeding occurred in 7 (7%) patients (5 ischemic strokes, 1 non-cerebrovascular arterial thromboembolism, 2 left atrial appendage thrombi) and 18 (19%) patients, respectively. At day 28, 27 patients (29%) died. Septic shock (hazard ratio [HR]: 2.36; 95% CI 1.06 to 5.29) and left ventricular systolic dysfunction (HR: 2.06; 95% CI 1.05 to 4.05) were independently associated with cardiovascular events. Conclusions: Transesophageal echocardiographic abnormalities are common in mechanically ventilated patients with sepsis and new-onset atrial fibrillation, but only left ventricular systolic dysfunction was associated with cardiovascular events at day 28

Transesophageal echocardiography for cardiovascular risk estimation in patients with sepsis and new-onset atrial fibrillation: a multicenter prospective pilot study

International audienceBackgroundEchocardiographic parameters have been poorly investigated for estimating cardiovascular risk in patients with sepsis and new-onset atrial fibrillation. We aim to assess the prevalence of transesophageal echocardiographic abnormalities and their relationship with cardiovascular events in mechanically ventilated patients with sepsis and new-onset atrial fibrillation.MethodsIn this prospective multicenter pilot study, left atrial/left atrial appendage (LA/LAA) dysfunction, severe aortic atheroma, and left ventricular systolic dysfunction were assessed using an initial transesophageal echocardiographic study, which was repeated after 48–72 h to detect LA/LAA thrombus formation. The study outcome was a composite of cardiovascular events at day 28, including arterial thromboembolic events (ischemic stroke, non-cerebrovascular arterial thromboembolism, LA/LAA thrombus), major bleeding, and all-cause death.ResultsThe study population comprised 94 patients (septic shock 63%; 35% women; median age 69 years). LA/LAA dysfunction, severe aortic atheroma, and left ventricular systolic dysfunction were detected in 17 (19%), 22 (24%), and 27 (29%) patients, respectively. At day 28, the incidence of cardiovascular events was 46% (95% confidence interval [CI]: 35 to 56). Arterial thromboembolic events and major bleeding occurred in 7 (7%) patients (5 ischemic strokes, 1 non-cerebrovascular arterial thromboembolism, 2 left atrial appendage thrombi) and 18 (19%) patients, respectively. At day 28, 27 patients (29%) died. Septic shock (hazard ratio [HR]: 2.36; 95% CI 1.06 to 5.29) and left ventricular systolic dysfunction (HR: 2.06; 95% CI 1.05 to 4.05) were independently associated with cardiovascular events.ConclusionsTransesophageal echocardiographic abnormalities are common in mechanically ventilated patients with sepsis and new-onset atrial fibrillation, but only left ventricular systolic dysfunction was associated with cardiovascular events at day 2