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Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning approach based on conventional magnetic resonance imaging (MRI) for non-invasive classification of SCLC vs. NSCLC brain metastases. Sixty-nine patients with brain metastasis of lung cancer origin were included. Of them, 44 patients had NSCLC and 25 patients had SCLC. Classification was performed with EfficientNet architecture on crop images of lesion areas and based on post-contrast T1-weighted, T2-weighted and FLAIR imaging input data. Evaluation of the model was carried out in a 5-fold cross-validation manner, and based on accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve. The best classification results were obtained with multiparametric MRI input data (T1WI+c+FLAIR+T2WI), with a mean overall accuracy of 0.90 ± 0.04, and F1 score of 0.92 ± 0.05 for NSCLC and 0.87 ± 0.08 for SCLC for the validation data and an accuracy of 0.87 ± 0.05, with an F1 score of 0.88 ± 0.05 for NSCLC and 0.85 ± 0.05 for SCLC for the test dataset. The proposed method provides an automatic noninvasive method for the classification of brain metastasis with high sensitivity and specificity for differentiation between NSCLC vs. SCLC brain metastases. It may be used as a diagnostic tool for improving decision-making in the treatment of patients with these metastases. Further studies on larger patient samples are required to validate the current results.

Creative thinking represents a major evolutionary mechanism that greatly contributed to the rapid advancement of the human species. The ability to produce novel and useful ideas, or original thinking, is thought to correlate well with unexpected, synchronous activation of several large-scale, dispersed cortical networks, such as the default network (DN). Despite a vast amount of correlative evidence, a causal link between default network and creativity has yet to be demonstrated. Surgeries for resection of brain tumors that lie in proximity to speech related areas are performed while the patient is awake to map the exposed cortical surface for language functions. Such operations provide a unique opportunity to explore human behavior while disrupting a focal cortical area via focal electrical stimulation. We used a novel paradigm of individualized direct cortical stimulation to examine the association between creative thinking and the DN. Preoperative resting-state fMRI was used to map the DN in individual patients. A cortical area identified as a DN node (study) or outside the DN (controls) was stimulated while the participants performed an alternate-uses-task (AUT). This task measures divergent thinking through the number and originality of different uses provided for an everyday object. Baseline AUT performance in the operating room was positively correlated with DN integrity. Direct cortical stimulation at the DN node resulted in decreased ability to produce alternate uses, but not in the originality of uses produced. Stimulation in areas that when used as network seed regions produced a network similar to the canonical DN was associated with reduction of creative fluency. Stimulation of areas that did not produce a default-like network (controls) did not alter creative thinking. This is the first study to causally link the DN and creative thinking.

The insula plays a fundamental role in a wide range of adaptive human behaviors, but its electrophysiological dynamics are poorly understood. Here, we used human intracranial electroencephalographic recordings to investigate the electrophysiological properties and hierarchical organization of spontaneous neuronal oscillations within the insula. We analyzed the neuronal oscillations of the insula directly and found that rhythms in the theta and beta frequency oscillations are widespread and spontaneously present. These oscillations are largely organized along the anterior–posterior (AP) axis of the insula. Both the left and right insula showed anterior-­to-posterior decreasing gradients for the power of oscillations in the beta frequency band. The left insula also showed a posterior-to-anterior decreasing frequency gradient and an anterior-to-posterior decreasing power gradient in the theta frequency band. In addition to measuring the power of these oscillations, we also examined the phase of these signals across simultaneous recording channels and found that the insula oscillations in the theta and beta bands are traveling waves. The strength of the traveling waves in each frequency was positively correlated with the amplitude of each oscillation. However, the theta and beta traveling waves were uncoupled to each other in terms of phase and amplitude, which suggested that insular traveling waves in the theta and beta bands operate independently. Our findings provide new insights into the spatiotemporal dynamics and hierarchical organization of neuronal oscillations within the insula, which, given its rich connectivity with widespread cortical regions, indicates that oscillations and traveling waves have an important role in intrainsular and interinsular communications.

Purpose Non-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular profile of the primary tumor and the brain metastases (BMs), identifying an individual patient’s EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure. We employed a deep learning (DL) method with the aim of noninvasive detection of the EGFR mutation status in NSCLC BM. Methods We retrospectively collected clinical, radiological, and pathological-molecular data of all the NSCLC patients who had been diagnosed with BMs and underwent resection of their BM during 2009–2019. The study population was then divided into two groups based upon EGFR mutational status. We further employed a DL technique to classify the two groups according to their preoperative magnetic resonance imaging features. Augmentation techniques, transfer learning approach, and post-processing of the predicted results were applied to overcome the relatively small cohort. Finally, we established the accuracy of our model in predicting EGFR mutation status of BM of NSCLC. Results Fifty-nine patients were included in the study, 16 patients harbored EGFR mutations. Our model predicted mutational status with mean accuracy of 89.8%, sensitivity of 68.7%, specificity of 97.7%, and a receiver operating characteristic curve value of 0.91 across the 5 validation datasets. Conclusion DL-based noninvasive molecular characterization is feasible, has high accuracy and should be further validated in large prospective cohorts.

Brain metastases are common in patients with advanced melanoma and constitute a major cause of morbidity and mortality. Between 40% and 60% of melanomas harbor BRAF mutations. Selective BRAF inhibitor therapy has yielded improvement in clinical outcome; however, genetic discordance between the primary lesion and the metastatic tumor has been shown to occur. Currently, the only way to characterize the genetic landscape of a brain metastasis is by tissue sampling, which carries risks and potential complications. The aim of this study was to investigate the use of radiomics analysis for non-invasive identification of BRAF mutation in patients with melanoma brain metastases, based on conventional magnetic resonance imaging (MRI) data. We applied a machine-learning method, based on MRI radiomics features for noninvasive characterization of the BRAF status of brain metastases from melanoma (BMM) and applied it to BMM patients from two tertiary neuro-oncological centers. All patients underwent surgical resection for BMM, and their BRAF mutation status was determined as part of their oncological work-up. Their routine preoperative MRI study was used for radiomics-based analysis in which 195 features were extracted and classified according to their BRAF status via a support vector machine. The BRAF status of 53 study patients, with 54 brain metastases (25 positive, 29 negative for BRAF mutation) was predicted with mean accuracy = 0.79 ± 0.13, mean precision = 0.77 ± 0.14, mean sensitivity = 0.72 ± 0.20, mean specificity = 0.83 ± 0.11 and with a 0.78 area under the receiver operating characteristic curve for positive BRAF mutation prediction. Radiomics-based noninvasive genetic characterization is feasible and should be further verified using large prospective cohorts.

The rewards that we get from our choices and actions can have a major influence on our future behavior. Understanding how reward biasing of behavior is implemented in the brain is important for many reasons, including the fact that diminution in reward biasing is a hallmark of clinical depression. We hypothesized that reward biasing is mediated by the anterior cingulate cortex (ACC), a cortical hub region associated with the integration of reward and executive control and with the etiology of depression. To test this hypothesis, we recorded neural activity during a biased judgment task in patients undergoing intracranial monitoring for either epilepsy or major depressive disorder. We found that beta (12–30 Hz) oscillations in the ACC predicted both associated reward and the size of the choice bias, and also tracked reward receipt, thereby predicting bias on future trials. We found reduced magnitude of bias in depressed patients, in whom the beta-specific effects were correspondingly reduced. Our findings suggest that ACC beta oscillations may orchestrate the learning of reward information to guide adaptive choice, and, more broadly, suggest a potential biomarker for anhedonia and point to future development of interventions to enhance reward impact for therapeutic benefit. Understanding how rewards can override our initial sensory judgments and influence decision-making is crucial. Here the authors show that the anterior cingulate cortex plays an important role in mediating reward biasing.

Research on the neural basis of major depressive disorder suggests that it is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neuronal systems lead to diminished emotion regulation and intrusive rumination. Subregions of the prefrontal cortex are thought to be sources of this disinhibition. However, due to limited opportunities for intracranial recordings from humans with major depression, this hypothesis has not been directly tested. Here, we use intracranial recordings from the dorsolateral prefrontal, orbitofrontal, and anterior cingulate cortices from patients with major depression to measure daily fluctuations in self-reported depression symptom severity. Results indicate that directed connectivity within the delta frequency band, which has been linked to cortical inhibition, transiently increases intensity during negative mood. Symptom severity also shifts as connectivity patterns within the left and right prefrontal cortices become imbalanced. Our findings support the overarching hypothesis that depression worsens with prefrontal disinhibition and functional imbalance between hemispheres. Low frequency brain waves convey information between regions. Here, the authors demonstrate that for patients with major depression, mood becomes more negative as low frequency waves increase intensity  across the prefrontal cortex.

Objective MR-guided laser interstitial thermal therapy (MRgLITT) is a minimally invasive technique for ablating brain lesions under real-time MRI feedback and control of the ablation process. The Medtronic Visualase system was recently approved for use in Europe and Israel. We report our initial technical experience using the system in the first 16 cases in which the system was used to ablate focal epileptogenic lesions. Methods We included all consecutive patients with intractable epilepsy who underwent MRgLITT procedures between 2018 and 2020. We reviewed medical charts and imaging studies of patients. Post-ablation MRIs were used to calculate ablation volumes. Results Seventeen MRgLITT procedures were performed in 16 patients. One cooling catheter/laser fiber assemblies were placed per patient. Indications for surgery were intractable epilepsy due to TLE ( n  = 7), suspected low-grade glioma ( n  = 4), radiological cortical dysplasia ( n  = 1), hypothalamic hamartoma ( n  = 1), and MR-negative foci ( n  = 3). Ablations were made using 30 to 70% of the maximal energy of the Visualase system. We used serial ablations as needed along the tract of the catheter by pulling back the optic fiber; the length of the lesion ranged between 7.4 and 38.1 mm. Ablation volume ranged between 0.27 and 6.78 mm 3 . Immediate post-ablation MRI demonstrated good ablation of the epileptic lesion in 16/17 cases. In one case with mesial temporal sclerosis, no ablation was performed due to suboptimal position of the catheter. That patient was successfully reoperated at a later date. Mean follow-up was 14.9 months (± 11.6 months). Eleven patients had follow-up longer than 12 months. Good seizure control (Engel I, A) was achieved in 7/11 patients (63%) and 1/11 (9%) had significant improvement in seizure frequency (Angle IIIa). Three patients (27%) did not experience improvement in their seizure frequency (Engel IV, B), and one of these patients died during the follow-up period from sudden unexpected death of epilepsy (SUDEP). No immediate or delayed neurological complications were documented in any of the cases during the follow-up period. Conclusions MRgLITT is a promising technique and can be used safely as an alternative to open resection in both lesional and non-lesional intractable epilepsy cases. In our local series, the success rate of epilepsy surgery was comparable to recent publications.

Direct electrical stimulation of the human brain has been used for numerous clinical and scientific applications. At present, however, little is known about how intracranial stimulation affects activity at the microscale. In this study, we recorded intracranial EEG data from a cohort of patients with medically refractory epilepsy as they completed a visual recognition memory task. During the memory task, brief trains of intracranial theta burst stimulation (TBS) were delivered to the basolateral amygdala (BLA). Using simultaneous microelectrode recordings, we isolated neurons in the hippocampus, amygdala, orbitofrontal cortex, and anterior cingulate cortex and tested whether stimulation enhanced or suppressed firing rates. Additionally, we characterized the properties of modulated neurons, clustered presumed excitatory and inhibitory neurons by waveform morphology, and examined the extent to which modulation affected memory task performance. We observed a subset of neurons (~30%) whose firing rate was modulated by TBS, exhibiting highly heterogeneous responses with respect to onset latency, duration, and direction of effect. Notably, location and baseline activity predicted which neurons were most susceptible to modulation, although the impact of this neuronal modulation on memory remains unclear. These findings advance our limited understanding of how focal electrical fields influence neuronal firing at the single-cell level.