Brain Activity is Influenced by How High Dimensional Data are Represented: An EEG Study of Scatterplot Diagnostic (Scagnostics) Measures

Visualization and visual analytic tools amplify one's perception of data, facilitating deeper and faster insights that can improve decision making. For multidimensional data sets, one of the most common approaches of visualization methods is to map the data into lower dimensions. Scatterplot matrices (SPLOM) are often used to visualize bivariate relationships between combinations of variables in a multidimensional dataset. However, the number of scatterplots increases quadratically with respect to the number of variables. For high dimensional data, the corresponding enormous number of scatterplots makes data exploration overwhelmingly complex, thereby hindering the usefulness of SPLOM in human decision making processes. One approach to address this difficulty utilizes Graph-theoretic Scatterplot Diagnostic (Scagnostics) to automatically extract a subset of scatterplots with salient features and of manageable size with the hope that the data will be sufficient for improving human decisions. In this paper, we use Electroencephalogram (EEG) to observe brain activity while participants make decisions informed by scatterplots created using different visual measures. We focused on 4 categories of Scagnostics measures: Clumpy, Monotonic, Striated, and Stringy. Our findings demonstrate that by adjusting the level of difficulty in discriminating between data sets based on the Scagnostics measures, different parts of the brain are activated: easier visual discrimination choices involve brain activity mostly in visual sensory cortices located in the occipital lobe, while more difficult discrimination choices tend to recruit more parietal and frontal regions as they are known to be involved in resolving ambiguities. Our results imply that patterns of neural activity are predictive markers of which specific Scagnostics measures most assist human decision making based on visual stimuli such as ours.

A Soft Housing Needle Ultrasonic Transducer for Focal Stimulation to Small Animal Brain

Conventional acoustic brain stimulators that transmit low frequency (< 1 MHz) bursts in a pulse repetition frequency with large-sized transducers are barely compatible with small animal models because of broad beam width, possible stimulation of auditory pathways, and blocking of field-of-view for in vivo imaging of brain hemodynamics and neuronal activities. A miniaturized ultrasound stimulator with higher stimulation frequencies will enhance spatial specificity and enable simultaneous eliciting and monitoring brain activities. Moreover, the use of non-periodic pulse sequences may reduce unintended stimulations on auditory cortex, which might be caused by transmitting periodic bursting patterns. A platform for ultrasound brain stimulations for small animal models, including a soft housing 10 MHz needle transducer with a beam size of 680 μm, random transmission sequences, and optical imaging systems, was developed. The platform can deliver focal stimulations to the visual and barrel cortex of mice and monitor subsequent brain activities. The stimulated sites in both the visual and primary somatosensory cortices (S1) showed approximately two to three times higher neuronal calcium signal levels than those in peripheral regions. Activities in the auditory cortex were elicited by periodic sequence stimulation, while it was reduced by 67 and 35% for barrel and visual cortex stimulation with the random sequence, respectively.

Histopathologic and Clinical Correlation of Aberrant Neuromagnetic Activities with Low to High Frequency of Gliomas

OBJECTIVE

To quantify the low- to high-frequency range of abnormal brain activities and assess the histopathologic and clinical correlation in patients with glioma.

METHODS

Twenty-five patients with glioma and 20 healthy controls were examined with a magnetoencephalography (MEG) system. MEG data collected in 6 frequency bands, including 1-4 Hz, 4-8 Hz, 8-12 Hz, 12-30 Hz, 30-45 Hz, and 55-75 Hz, were analyzed by neuropathology to assess neuromagnetic signatures of glioma grade.

RESULTS

Compared with controls, patients with glioma showed greatly altered brain activities in 4-8 Hz, 8-12 Hz, and 55-75 Hz. Magnetic source power of brain activities in 4-8 Hz and 55-75 Hz in patients with high-grade gliomas significantly differed from that in patients with low-grade gliomas. The magnitude of source power of brain activities in 4-8 Hz and 55-75 Hz had marked correlation with the grading of gliomas in histopathological analyses.

CONCLUSIONS

The degree of neuromagnetic source abnormality is a potential biomarker for noninvasive assessment of glioma grade. Because MEG tests can be performed noninvasively and preoperatively, MEG may play an important role in clinical biopsies and surgical planning for patients with brain gliomas in the future.

Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

Objective

Epilepsy is a neuronal disorder for which the electrical discharge in the brain is synchronized, abnormal and excessive. To detect the epileptic seizures and to analyse brain activities during different mental states, various methods in non-linear dynamics have been proposed. This study is an attempt to quantify the complexity of control and epileptic subject with and without seizure as well as to distinguish eye-open (EO) and eye-closed (EC) conditions using threshold-based symbolic entropy.

Methods

The threshold-dependent symbolic entropy was applied to distinguish the healthy and epileptic subjects with seizure and seizure-free intervals (i.e. interictal and ictal) as well as to distinguish EO and EC conditions. The original time series data was converted into symbol sequences using quantization level, and word series of symbol sequences was generated using a word length of three or more. Then, normalized corrected Shannon entropy (NCSE) was computed to quantify the complexity. The NCSE values were not following the normal distribution, and the non-parametric Mann–Whitney–Wilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. The values of NCSE were presented in a form of topographic maps to show significant brain regions during EC and EO conditions. The results of the study were compared to those of the multiscale entropy (MSE).

Results

The results indicated that the dynamics of healthy subjects are more complex compared to epileptic subjects (during seizure and seizure-free intervals) in both EO and EC conditions. The comparison of the dynamics of epileptic subjects revealed that seizure-free intervals are more complex than seizure intervals. The dynamics of healthy subjects during EO conditions are more complex compared to those during EC conditions. Further, the results clearly demonstrated that threshold-dependent symbolic entropy outperform MSE in distinguishing different physiological and pathological conditions.

Conclusion

The threshold symbolic entropy has provided improved accuracy in quantifying the dynamics of healthy and epileptic subjects during EC an EO conditions for each electrode compared to the MSE.