Frontiers in Biomedical Technologies
Volume:6 Issue: 2, Spring 2019

Like other parts of the human body, brain has been threatened by various diseases and disorders. In addition to its strong protection by skull bone, the complexity of its function made the brain one of the less known organs of human body.  Moreover the brain is the source of intelligence in the hierarchy of human actions and decoding the brain function is a golden target for artificial intelligence.
The discovery of the brain structure and function so far is based on several core modalities. I will try to cover the basic techniques of functional neuroimaging here, to highlight the advantages and limitations and also give a perspective on their future applications.

One of the most well-known multimodality techniques is the integration of EEG and fMRI datasets. Convolution of EEG signals with hemodynamic response function is one of the most important methods to consider the effect of HRF in the fusion of EEG and fMRI data. However, the latencies and amplitudes of ERPs and fMRI spatial components are affected by the low pass filtering effect of HRF in each trial. In this paper, we have proposed a new method based on Advanced Coupled Matrix Tensor Factorization model to jointly factorize the EEG tensor and fMRI matrix while we simultaneously remove the effect of HRF through decomposition of fMRI dataset. Applying the proposed method to an auditory oddball paradigm of simultaneous EEG-fMRI recording, the well-known ERP of oddball paradigm and the corresponding fMRI spatial maps are estimated. The results demonstrate that our proposed approach is strongly capable of extracting the ERPs and their corresponding fMRI spatial components, while simultaneously estimates the trial to trial variations of these factors with accurate amplitude and latency in each trial.

A major obstacle in brain tumor surgery is to precisely identify the boundaries of the tumor for maximal elimination without any residual tumoral tissues. Gliomas have the same color and texture as the normal brain tissues, which impose a major burden for neurosurgery. Recently, 3 Tesla Magnetic Resonance Imaging (MRI) scanners have demonstrated the potential for sub-millimeter anatomical imaging. Considering the growing importance of small animal imaging in pre-clinical research, the purpose of the present study was to evaluate the ability of contrast-enhanced MRI to determine glioma tumor volume in comparison with Caliper and Histology Thirteen rats were anesthetized, and fixed in stereotactic frame. C6 cells were injected into the right cortex of rats. Fourteen days later, rats were evaluated with 3 Tesla MRI equipped with a head receiver coil. Two hours before imaging, the Magnetic Nano-Particles (MNPs) were injected  through the tail of the rats, before they were placed in a magnetic field (1.3 Tesla). Axial and coronal sections of the brain were obtained with a T2-weighted turbo-spin-echo protocol. Finally, rats were sacrificed and their brains were fixed in formalin for measuring the tumor volume with Caliper, and Hematoxylin and Eosin (H&E) staining. The results of calculating tumor volume by MRI, caliper and H&E were 72.6±7.3mm3, 61±11.1mm3 and 76.4±7.8mm3, respectively. There was no significant difference in tumor volume between MRI and H&E (P= 0.24), while, there was a significant difference between MRI and caliper data (P< 0.05). According to the results, it is not easy to discriminate the delicate border between normal tissue and Glioma tumor by the naked eye. However, H&E staining may help separate the boundary between normal tissue and tumor with a high precision at the cellular level. Comparing the results of 3 Tesla MRI with both the results from H&E and caliper indicated that there was not a significant difference between the findings of MRI an H&E staining, thus, MRI could be recognized as an acceptable method for non-invasive tumor volume measurement.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, characterized by pervasive symptoms, as in DSM-V. It is identified and associated with a number of atypicalities, including difficulties in face memory, centralized interest, and abnormal body movements. Individuals high in ASD show problems in face processing, gaze, and expression that arise from inappropriate brain functioning in social behaviors and communication skills. When a sensory stimulus is repeated, the excited neural signal is always smaller than its first observation. This phenomenon has been observed for many sensory states and stimuli using different methods. The present study was conducted to investigate the repression of facial image reproduction with the mediating role of time in adults with low and high autism-like traits. This research was carried out with a quantitative method approach in the form of a descriptive design in two groups with low and high ASD. For this purpose, the autism spectrum quotient, cognitive task for suppressing repetitive face images, and EEG were used. The sample consisted of 30 male undergraduate and postgraduate students aged between 18 and 35. As a result, the research findings showed a significant statistical difference between the two groups with low and high ASD in terms of cognitive and EEG correlates in suppressing the repetition of facial images. Specifically, an interactive effect of time (short or long intervals), consistency of stimuli (repeated or not), and autism spectrum (high or low) was significant (F1, 28 = 4.53, p = 0.04). This was indexed by a lack of N2 and P3 in those with high compared to low ASD. The possible insensitivities to repetition might be due to unused extra neural resources in high ASD, close to brain areas involved in face processing.

Our purpose in this study was to investigate the distinction between emotional states and the performance of the brain during different feelings by using temporal network theory. For investigating the distinction between emotions, we chose functional magnetic resonance imaging data acquired during the display of an emotional audio-movie. In order to derive dynamic functional connectivity and create time-graphlets, we used spatial distance method and for studying the features of the temporal network, we applied different temporal network measures. Considering statistical comparisons, two global measures of temporal efficiency and reachability latency showed a significant difference between at least one pair of emotional states and we observed different meaningful regions in each temporal centrality measure. The results of this analytic method showed that the brain network pattern during the expression of different emotional states is different compared to one another and also varies through time.

Alzheimer’s disease is a neurodegenerative disease that begins before clinical symptoms emerge. Amyloid-beta plaques and tau neurofibrillary tangles are the hallmark lesions of Alzheimer’s Disease (AD). Amyloid-beta plaques deposition is associated with increased hippocampal volume loss. The tissue volume measures reflect multiple underlying pathologies contributing to neurodegeneration, of which are the most characteristics of AD. Anatomical atrophy, as evidenced using Magnetic Resonance Imaging (MRI), is one of the most validated, easily accessible and widely used biomarkers of AD. Measurements of whole brain and hippocampal atrophy rates from serial structural MRI are potential markers of the underlying neuroaxonal damage and disease progression in AD. In this study, we extract automatically subcortical brain structures in AD and control subjects. In this study we used 20 images (10 AD patients and 10 controls) taken from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. We obtained volumes of Cerebrospinal Fluid (CSF), White Matter (WM), Grey Matter (GM), brain hemispheres, cerebellum and brainstem using volBrain pipeline. Subcortical brain structure segments and related volumes and label maps information were extracted. We compared left and right sides of some of the important brain area in AD for obtaining a biomarker with brain atrophy. Amygdala, caudate and hippocampus have shown to be undergone atrophy in AD.

We provided volume information of some intracranial areas such as brain hemispheres, cerebellum and brainstem.

The results showed smaller hippocampal volume in AD patients compared to the controls. In addition to hippocampus, similar atrophy is also observable in amygdala and caudate.

Nowadays, the number of people diagnosed with movement disorders is increasing. Therefore, the evaluation of brain activity during motor task performance has attracted the attention of researchers in recent years. Functional Near-Infrared Spectroscopy (fNIRS) is a useful method that measures hemodynamic changes in the brain cortex based on optical principles. The purpose of this study was to evaluate the brain’s cortical activation in passive movement of the wrist. In current study, the activation of the brain's motor cortex during passive movement of the right wrist was investigated. To perform this study, ten healthy young right-handed volunteers were chosen. The required data were collected using a commercial 48-channel continuous wave fNIRS machine, using two different wavelengths of 765 and 855 nm at 10 Hz sampling rate. Analysis of collected data showed that the brain's motor cortex during passive motion was significantly activated (p≤0.05) compared to rest. Motor cortex activation patterns depending on passive movement direction were separated. In different directions of wrist movement, the maximum activation was recorded at the primary motor cortex (M1). The present study has investigated the ability of fNIRS to evaluate cortical activation during passive movement of the wrist. Analysis of recording signals showed that different directions of movement have specific activation patterns in the motor cortex.

Recently, data from functional magnetic resonance imaging in the field of neuroscience have been strongly considered for the modeling of cognitive activities. Therefore, the use of a suitable method is important for evaluating functional magnetic resonance imaging data. Regression dynamic causal modeling is introduced as a new version of dynamic causal modeling in order to extract and derive effective connectivity in functional magnetic resonance imaging data. We used this method to investigate the distinction in effective connectivity between the pair of emotional states. In this article, the effective connectivity between regions and activity of brain regions of interest during the application of a particular type of stimulation, which simulates the emotions created during human life, is examined in the form of an audio-movie. To do this, we applied the regression dynamic causal modeling method to a network consisting of 18 regions of interest that named the mixed model. In the mixed model, the distinction between happiness-anger, happiness-fear, and happiness-love was more intense. Finally, significant effective connectivities were observed in the auditory regions and regions related to emotion processing. Ultimately, we could represent the distinction between emotions by applying the regression dynamic causal modeling to the mixed model.