Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment.

OBJECTIVE: The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection. METHODS: Seventeen patients with diffuse nonenhancing glioma (ages 22-56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery. RESULTS: Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10-3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10-4, respectively). CONCLUSIONS: Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients' memory recovery.We thank all patients for generous involvement in the study. We also thank to Luca Villa, Jessica Ingham, Alexa Mcdonald for their contribution to the study. This research was supported by The Brain Tumour Charity and the Guarantors of Brai

Intraoperative mapping of executive function using electrocorticography for patients with low-grade gliomas

Funder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272; Grant(s): Clinician Scientist Award 35 (ref: NIHR/CS/009/011)Abstract: Background: Intraoperative functional mapping with direct electrical stimulation during awake surgery for patients with diffuse low-grade glioma has been used in recent years to optimize the balance between surgical resection and quality of life following surgery. Mapping of executive functions is particularly challenging because of their complex nature, with only a handful of reports published so far. Here, we propose the recording of neural activity directly from the surface of the brain using electrocorticography to map executive functions and demonstrate its feasibility and potential utility. Methods: To track a neural signature of executive function, we recorded neural activity using electrocorticography during awake surgery from the frontal cortex of three patients judged to have an appearance of diffuse low-grade glioma. Based on existing functional magnetic resonance imaging (fMRI) evidence from healthy participants for the recruitment of areas associated with executive function with increased task demands, we employed a task difficulty manipulation in two counting tasks performed intraoperatively. Following surgery, the data were extracted and analyzed offline to identify increases in broadband high-gamma power with increased task difficulty, equivalent to fMRI findings, as a signature of activity related to executive function. Results: All three patients performed the tasks well. Data were recorded from five electrode strips, resulting in data from 15 channels overall. Eleven out of the 15 channels (73.3%) showed significant increases in high-gamma power with increased task difficulty, 26.6% of the channels (4/15) showed no change in power, and none of the channels showed power decrease. High-gamma power increases with increased task difficulty were more likely in areas that are within the canonical frontoparietal network template. Conclusions: These results are the first step toward developing electrocorticography as a tool for mapping of executive function complementarily to direct electrical stimulation to guide resection. Further studies are required to establish this approach for clinical use

Data from: Predicting forest insect flight activity: a Bayesian network approach

Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model’s predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways

Predicting forest insect flight activity: A Bayesian network approach

<div><p>Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, <i>Hylurgus ligniperda</i>, <i>Hylastes ater</i>, and <i>Arhopalus ferus</i> in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the <i>H</i>. <i>ater</i> and <i>A</i>. <i>ferus</i> Bayesian network models had the best fit for low Type I and overall errors, and <i>H</i>. <i>ligniperda</i> had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on <i>H</i>. <i>ligniperda</i> flight activity predictions, whereas time of day and year had the greatest influence on <i>H</i>. <i>ater</i> and <i>A</i>. <i>ferus</i> activity. Type II model errors for the prediction of no flight activity is improved by increasing the model’s predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.</p></div

Results from the final General Additive Models (GAMs) for the flight activity of <i>H</i>. <i>ligniperda</i>.

<p>GAMs have a parametric component and a smoothing part, hence the distinction between parametric coefficients and the smoothing terms. s() = smooth term for a continuous variable, <i>SE</i> = standard error of the estimate, <i>t</i> = <i>t</i>-statistic, <i>P</i> = <i>P</i>-value, <i>edf</i> = estimated degrees of freedom, <i>F</i> = <i>F</i>-statistic. Wdspd = Wind speed, PAR = Photon flux density, and RH = Relative humidity. Significant values are denoted with P <0.05 = *, P <0.01 = **, P <0.001 = ***.</p

Summary of BN model performance.

<p>Model performance is assessed at different predictive thresholds with both calibration (entire dataset) and validation (4-fold cross validation) results presented.</p

Number of positive trap catch hours as a function of time since sunrise in hourly bins for <i>H</i>. <i>ligniperda</i> and <i>H</i>. <i>ater</i> and for time since sunset for <i>A</i>. <i>ferus</i>.

<p>Because day length varied as a function of the day of the year during the study a range is provided that encompasses the period when sunrise or sunset occurred. Dashed lines indicate the period where sunset occurred as a function of time since sunrise for <i>H</i>. <i>ligniperda</i> and <i>H</i>. <i>ater</i>. Similarly for the nocturnal <i>A</i>. <i>ferus</i> these dashed lines indicate the period when sunrise occurred as a function of time since sunset.</p

False positive (Type I error) rates as a function of the threshold required for the model to predict ‘Yes’, and false negative (Type II error) rates as a function of the threshold required for the model to predict ‘No’ for <i>H</i>. <i>ligniperda</i>, <i>H</i>. <i>ater</i>, and <i>A</i>. <i>ferus</i>.

<p>Red lines indicate the relationship for the calibration dataset (i.e., full casefile), blue shading indicates the range of the first standard deviation for 100 runs of 4-fold cross validation, with the inner white line denoting mean outcomes. Yellow indicates the maximum and minimum values observed during those 100 runs. The green curve represents the number of standard deviations between the calibration and the average of the 100 runs of 4-fold cross validation.</p

Modified receiver operating curve (ROC) showing model predictions of the true negative state as a function of the type II (false negative) error rate.

<p>Modified receiver operating curve (ROC) showing model predictions of the true negative state as a function of the type II (false negative) error rate.</p