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Decoding of visual information from human brain activity: A review of fMRI and EEG studies

Zafar R, Malik AS, Kamel N, Dass SC, Abdullah JM, Reza F, et al.

J Integr Neurosci, 2015 Jun;14(2):155-68.
PMID: 25939499 DOI: 10.1142/S0219635215500089

Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding. This paper concludes that it's not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.

Matched MeSH terms: Brain/physiology*
Long live the queen

Law YH

Science, 2021 Mar 26;371(6536):1302-1305.
PMID: 33766870 DOI: 10.1126/science.371.6536.1302
Matched MeSH terms: Brain/physiology
Association between apoptotic neural tissue and cell proliferation in the adult teleost brain

Lim FT, Ogawa S, Parhar IS

Brain Res, 2016 11 01;1650:60-72.
PMID: 27568467 DOI: 10.1016/j.brainres.2016.08.033

Injury to neuronal tissues in the central nervous system (CNS) of mammals results in neural degeneration and sometime leads to loss of function, whereas fish retain a remarkable potential for neuro-regeneration throughout life. Thus, understanding the mechanism of neuro-regeneration in fish CNS would be useful to improve the poor neuro-regenerative capability in mammals. In the present study, we characterized a neuro-regenerative process in the brain of a cichlid, tilapia, Oreochromis niloticus. Morphological observations showed that the damaged brain region (habenula) successfully regrew and reinnervated axonal projections by 60 days post-damage. A fluorescent carbocyanine tracer, DiI tracing revealed a recovery of the major neuronal projection from the regenerated habenula to the interpenduncular nucleus by 60 days post-damage. TUNEL assay showed a significant increase of apoptotic cells (~234%, P<0.01) at one day post-damage, while the number of bromodeoxyuridine (BrdU)-positive proliferative cells were significantly increased (~92%, P<0.05) at 7 days post-damage compared with sham-control fish. To demonstrate a potential role of apoptotic activity in the neuro-regeneration, effects of degenerative neural tissue on cell proliferation were examined in vivo. Implantation of detached neural but not non-neural tissues into the cranial cavity significantly (P<0.01) increased the number of BrdU-positive cells nearby the implantation regions at 3 days after the implantation. Furthermore, local injection of the protein extract and cerebrospinal fluid collected from injured fish brain significantly induced cell proliferation in the brain. These results suggest that factor(s) derived from apoptotic neural cells may play a critical role in the neuro-regeneration in teleost brain.

Matched MeSH terms: Brain/physiology
Fulltext Editorial: Steroids and the Brain

Ubuka T, Trudeau VL, Parhar I

Front Endocrinol (Lausanne), 2020;11:366.
PMID: 32582033 DOI: 10.3389/fendo.2020.00366
Matched MeSH terms: Brain/physiology*
Isolation and characterization of primary microglia from post-natal murine brain tissues: a comparison of two methods

Jose S, Tan SW, Tong CK, Vidyadaran S

Cell Biol Int, 2015 Dec;39(12):1355-63.
PMID: 26194799 DOI: 10.1002/cbin.10516

Microglia are resident macrophages of the central nervous system (CNS). Apart from playing vital roles as sentinel cells, they are crucial in physiological processes such as synaptic pruning during brain development. CNS disorders require an understanding of the contribution of each cellular compartment to the pathogenesis. Elucidating the role of microglia in disease development and progression in the intricate CNS environment is technically challenging and requires the establishment of reliable, reproducible techniques to isolate and culture microglia. A number of different protocols have been developed for isolation of neonatal microglia and here we compare two widely used methods, namely, mild trypsinization and EasySep® magnetic separation. EasySep® magnetic separation provided higher microglia yield, and flow cytometric evaluation of CD11b and F4/80 markers revealed that EasySep® separation method also produced significantly higher purity compared to mild trypsinization. Microglia isolated using EasySep® separation method were functional, as demonstrated by the generation of nitric oxide, IL-6, TNF-α, and MCP-1 in response to lipopolysaccharide stimulation. In summary, this study has revealed that magnetic separation is superior to mild trypsinization in terms of yield and purity of microglia.

Matched MeSH terms: Brain/physiology*
Fulltext Health and Pleasure in Consumers' Dietary Food Choices: Individual Differences in the Brain's Value System

Petit O, Merunka D, Anton JL, Nazarian B, Spence C, Cheok AD, et al.

PLoS One, 2016;11(7):e0156333.
PMID: 27428267 DOI: 10.1371/journal.pone.0156333

Taking into account how people value the healthiness and tastiness of food at both the behavioral and brain levels may help to better understand and address overweight and obesity-related issues. Here, we investigate whether brain activity in those areas involved in self-control may increase significantly when individuals with a high body-mass index (BMI) focus their attention on the taste rather than on the health benefits related to healthy food choices. Under such conditions, BMI is positively correlated with both the neural responses to healthy food choices in those brain areas associated with gustation (insula), reward value (orbitofrontal cortex), and self-control (inferior frontal gyrus), and with the percent of healthy food choices. By contrast, when attention is directed towards health benefits, BMI is negatively correlated with neural activity in gustatory and reward-related brain areas (insula, inferior frontal operculum). Taken together, these findings suggest that those individuals with a high BMI do not necessarily have reduced capacities for self-control but that they may be facilitated by external cues that direct their attention toward the tastiness of healthy food. Thus, promoting the taste of healthy food in communication campaigns and/or food packaging may lead to more successful self-control and healthy food behaviors for consumers with a higher BMI, an issue which needs to be further researched.

Matched MeSH terms: Brain/physiology*
A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects

Cacha LA, Parida S, Dehuri S, Cho SB, Poznanski RR

J Integr Neurosci, 2016 Dec;15(4):593-606.
PMID: 28093025 DOI: 10.1142/S0219635216500345

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

Matched MeSH terms: Brain/physiology*
Fulltext Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task

Goto N, Mushtaq F, Shee D, Lim XL, Mortazavi M, Watabe M, et al.

Biol Psychol, 2017 09;128:11-20.
PMID: 28666891 DOI: 10.1016/j.biopsycho.2017.06.004

We investigated whether well-known neural markers of selective attention to motivationally-relevant stimuli were modulated by variations in subjective preference towards consumer goods in a virtual shopping task. Specifically, participants viewed and rated pictures of various goods on the extent to which they wanted each item, which they could potentially purchase afterwards. Using the event-related potentials (ERP) method, we found that variations in subjective preferences for consumer goods strongly modulated positive slow waves (PSW) from 800 to 3000 milliseconds after stimulus onset. We also found that subjective preferences modulated the N200 and the late positive potential (LPP). In addition, we found that both PSW and LPP were modulated by subsequent buying decisions. Overall, these findings show that well-known brain event-related potentials reflecting selective attention processes can reliably index preferences to consumer goods in a shopping environment. Based on a large body of previous research, we suggest that early ERPs (e.g. the N200) to consumer goods could be indicative of preferences driven by unconditional and automatic processes, whereas later ERPs such as the LPP and the PSW could reflect preferences built upon more elaborative and conscious cognitive processes.

Matched MeSH terms: Brain/physiology*
A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA

Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, et al.

Australas Phys Eng Sci Med, 2018 Sep;41(3):633-645.
PMID: 29948968 DOI: 10.1007/s13246-018-0656-5

Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.

Matched MeSH terms: Brain/physiology*
Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity

Jatoi MA, Kamel N, Musavi SHA, López JD

Curr Med Imaging Rev, 2019;15(2):184-193.
PMID: 31975664 DOI: 10.2174/1573405613666170629112918

BACKGROUND: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms.
METHODS: Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared.
RESULTS: The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB.
CONCLUSION: In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

Matched MeSH terms: Brain/physiology*
Proteomics Identification of Potential Candidates Involved in Cell Proliferation for Early Stage of Brain Regeneration in the Adult Zebrafish

Lim FT, Ogawa S, Smith AI, Parhar IS

Zebrafish, 2017 Feb;14(1):10-22.
PMID: 27797681 DOI: 10.1089/zeb.2016.1319

The central nervous system (CNS) of the non-mammalian vertebrates has better neuroregenerative capability as compared with the mammalian CNS. Regeneration of habenula was observed 40 days after damage in zebrafish. During the early stage of regeneration, we found a significant increase of apoptotic cells on day-1 post-damage and of proliferative cells on day-3 post-damage. To identify the molecular factor(s) involved in the early stages of neuroregeneration, differentially expressed proteins during sham, 20- and 40-h post-habenula damage were investigated by proteomic approach by using two-dimensional differential gel electrophoresis (2D-DIGE) coupled with Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight (MALDI-ToF) and tandem mass spectrometry. Protein profiles revealed 17 differentially (>1.5-fold) expressed proteins: 10 upregulated, 4 downregulated, 2 proteins were found to be downregulated at the early stage but upregulated at a later stage, and 1 protein was found to be upregulated at 2 different time points. All proteins identified can be summarized under few molecular processes involved in the early stages of neuroregeneration in zebrafish CNS: apoptosis regulation (Wnt inhibitory factor 1 [WIF1]), neuroprotection (metallothionein), cell proliferation (Spred2, ependymin, Lhx1, and Wnts), differentiation (Spred2, Lhx9, and Wnts), and morphogenesis (cytoplasmic actins and draculin). These protein profiling results suggest that drastic molecular changes occur in the neuroregenerative process during this period, which includes cell proliferation, differentiation, and protection.

Matched MeSH terms: Brain/physiology*
Fulltext The Effects of Brain Breaks on Motives of Participation in Physical Activity among Primary School Children in Malaysia

Hajar MS, Rizal H, Kueh YC, Muhamad AS, Kuan G

Int J Environ Res Public Health, 2019 Jul 02;16(13).
PMID: 31269644 DOI: 10.3390/ijerph16132331

Brain breaks is a physical activity (PA) video designed for school settings that is used to stimulate student's health and learning. The purpose of this study is to measure the effects of brain breaks on motives of participation in PA among primary school children in Malaysia. Purposive sampling was used to divide 159 male and 176 female students aged 10 to 11 years old, mean (standard deviation (SD)) = 10.51 (0.50), from two schools in Kelantan, Malaysia into intervention (n = 183) and control (n = 152) groups. Students undertook brain breaks activities on school days (five minutes per session) spread out for a period of four months. Mixed factorial analysis of variance (ANOVA) was used to test the students' motives of participation in PA for pre-, mid-, and post-tests using the Physical Activity and Leisure Motivation Scale-Youth-Malay (PALMS-Y-M). Mixed factorial ANOVA showed significant changes in enjoyment, F(2, 392) = 8.720, p-value (ηp2) = 0.001 (0.043); competitiveness, F(2, 195) = 4.364, p-value (ηp2) = 0.014 (0.043); appearance, F(2, 392) = 5.709, p-value (ηp2) = 0.004 (0.028); and psychological condition, F(2, 392) = 4.376, p-value (ηp2) = 0.013 (0.022), whereas mastery, affiliation, and physical condition were not significant (p < 0.05). Further post-hoc analysis revealed a significant downward trend in the control group (p < 0.05). Brain breaks is successful in maintaining students' motives for PA in four of the seven factors.

Matched MeSH terms: Brain/physiology
Fulltext In search of a reliable electrophysiological marker of oculomotor inhibition of return

Satel J, Hilchey MD, Wang Z, Reiss CS, Klein RM

Psychophysiology, 2014 Oct;51(10):1037-45.
PMID: 24976355 DOI: 10.1111/psyp.12245

Inhibition of return (IOR) operationalizes a behavioral phenomenon characterized by slower responding to cued, relative to uncued, targets. Two independent forms of IOR have been theorized: input-based IOR occurs when the oculomotor system is quiescent, while output-based IOR occurs when the oculomotor system is engaged. EEG studies forbidding eye movements have demonstrated that reductions of target-elicited P1 components are correlated with IOR magnitude, but when eye movements occur, P1 effects bear no relationship to behavior. We expand on this work by adapting the cueing paradigm and recording event-related potentials: IOR is caused by oculomotor responses to central arrows or peripheral onsets and measured by key presses to peripheral targets. Behavioral IOR is observed in both conditions, but P1 reductions are absent in the central arrow condition. By contrast, arrow and peripheral cues enhance Nd, especially over contralateral electrode sites.

Matched MeSH terms: Brain/physiology*
Drowsiness detection during different times of day using multiple features

Sahayadhas A, Sundaraj K, Murugappan M

Australas Phys Eng Sci Med, 2013 Jun;36(2):243-50.
PMID: 23719977 DOI: 10.1007/s13246-013-0200-6

Driver drowsiness has been one of the major causes of road accidents that lead to severe trauma, such as physical injury, death, and economic loss, which highlights the need to develop a system that can alert drivers of their drowsy state prior to accidents. Researchers have therefore attempted to develop systems that can determine driver drowsiness using the following four measures: (1) subjective ratings from drivers, (2) vehicle-based measures, (3) behavioral measures and (4) physiological measures. In this study, we analyzed the various factors that contribute towards drowsiness. A total of 15 male subjects were asked to drive for 2 h at three different times of the day (00:00-02:00, 03:00-05:00 and 15:00-17:00 h) when the circadian rhythm is low. The less intrusive physiological signal measurements, ECG and EMG, are analyzed during this driving task. Statistically significant differences in the features of ECG and sEMG signals were observed between the alert and drowsy states of the drivers during different times of day. In the future, these physiological measures can be fused with vision-based measures for the development of an efficient drowsiness detection system.

Matched MeSH terms: Brain/physiology*
Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design

Huan NJ, Palaniappan R

J Neural Eng, 2004 Sep;1(3):142-50.
PMID: 15876633

In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

Matched MeSH terms: Brain/physiology*
Fulltext A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals

Bamatraf S, Hussain M, Aboalsamh H, Qazi EU, Malik AS, Amin HU, et al.

Comput Intell Neurosci, 2016;2016:8491046.
PMID: 26819593 DOI: 10.1155/2016/8491046

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

Matched MeSH terms: Brain/physiology*
Fulltext Genetic Differences in the Immediate Transcriptome Response to Stress Predict Risk-Related Brain Function and Psychiatric Disorders

Arloth J, Bogdan R, Weber P, Frishman G, Menke A, Wagner KV, et al.

Neuron, 2015 Jun 03;86(5):1189-202.
PMID: 26050039 DOI: 10.1016/j.neuron.2015.05.034

Depression risk is exacerbated by genetic factors and stress exposure; however, the biological mechanisms through which these factors interact to confer depression risk are poorly understood. One putative biological mechanism implicates variability in the ability of cortisol, released in response to stress, to trigger a cascade of adaptive genomic and non-genomic processes through glucocorticoid receptor (GR) activation. Here, we demonstrate that common genetic variants in long-range enhancer elements modulate the immediate transcriptional response to GR activation in human blood cells. These functional genetic variants increase risk for depression and co-heritable psychiatric disorders. Moreover, these risk variants are associated with inappropriate amygdala reactivity, a transdiagnostic psychiatric endophenotype and an important stress hormone response trigger. Network modeling and animal experiments suggest that these genetic differences in GR-induced transcriptional activation may mediate the risk for depression and other psychiatric disorders by altering a network of functionally related stress-sensitive genes in blood and brain.

Matched MeSH terms: Brain/physiology*
A new brain-computer interface design using fuzzy ARTMAP

Palaniappan R, Paramesran R, Nishida S, Saiwaki N

IEEE Trans Neural Syst Rehabil Eng, 2002 Sep;10(3):140-8.
PMID: 12503778

This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.

Matched MeSH terms: Brain/physiology
Fulltext Gut microbiota composition during infancy and subsequent behavioural outcomes

Loughman A, Ponsonby AL, O'Hely M, Symeonides C, Collier F, Tang MLK, et al.

EBioMedicine, 2020 Feb;52:102640.
PMID: 32062351 DOI: 10.1016/j.ebiom.2020.102640

BACKGROUND: Despite intense interest in the relationship between gut microbiota and brain development, longitudinal data from human studies are lacking. This study aimed to investigate the relationship between the composition of gut microbiota during infancy and subsequent behavioural outcomes.
METHODS: A subcohort of 201 children with behavioural outcome measures was identified within a longitudinal, Australian birth-cohort study. The faecal microbiota were analysed at 1, 6, and 12 months of age. Behavioural outcomes were measured at 2 years of age.
FINDINGS: In an unselected birth cohort, we found a clear association between decreased normalised abundance of Prevotella in faecal samples collected at 12 months of age and increased behavioural problems at 2 years, in particular Internalizing Problem scores. This association appeared independent of multiple potentially confounding variables, including maternal mental health. Recent exposure to antibiotics was the best predictor of decreased Prevotella.
INTERPRETATION: Our findings demonstrate a strong association between the composition of the gut microbiota in infancy and subsequent behavioural outcomes; and support the importance of responsible use of antibiotics during early life.
FUNDING: This study was funded by the National Health and Medical Research Council of Australia (1082307, 1147980, 1129813), The Murdoch Children's Research Institute, Barwon Health, Deakin University, Perpetual Trustees, and The Shepherd Foundation. The funders had no involvement in the data collection, analysis or interpretation, trial design, recruitment or any other aspect pertinent to the study.

Matched MeSH terms: Brain/physiology
Gene and protein expression profiles of JAK-STAT signalling pathway in the developing brain of the Ts1Cje down syndrome mouse model

Lee HC, Md Yusof HH, Leong MP, Zainal Abidin S, Seth EA, Hewitt CA, et al.

Int J Neurosci, 2019 Sep;129(9):871-881.
PMID: 30775947 DOI: 10.1080/00207454.2019.1580280

Aims: The JAK-STAT signalling pathway is one of the key regulators of pro-gliogenesis process during brain development. Down syndrome (DS) individuals, as well as DS mouse models, exhibit an increased number of astrocytes, suggesting an imbalance of neurogenic-to-gliogenic shift attributed to dysregulated JAK-STAT signalling pathway. The gene and protein expression profiles of JAK-STAT pathway members have not been characterised in the DS models. Therefore, we aimed to profile the expression of Jak1, Jak2, Stat1, Stat3 and Stat6 at different stages of brain development in the Ts1Cje mouse model of DS. Methods: Whole brain samples from Ts1Cje and wild-type mice at embryonic day (E)10.5, E15, postnatal day (P)1.5; and embryonic cortex-derived neurospheres were collected for gene and protein expression analysis. Gene expression profiles of three brain regions (cerebral cortex, cerebellum and hippocampus) from Ts1Cje and wild-type mice across four time-points (P1.5, P15, P30 and P84) were also analysed. Results: In the developing mouse brain, none of the Jak/Stat genes were differentially expressed in the Ts1Cje model compared to wild-type mice. However, Western blot analyses indicated that phosphorylated (p)-Jak2, p-Stat3 and p-Stat6 were downregulated in the Ts1Cje model. During the postnatal brain development, Jak/Stat genes showed complex expression patterns, as most of the members were downregulated at different selected time-points. Notably, embryonic cortex-derived neurospheres from Ts1Cje mouse brain expressed lower Stat3 and Stat6 protein compared to the wild-type group. Conclusion: The comprehensive expression profiling of Jak/Stat candidates provides insights on the potential role of the JAK-STAT signalling pathway during abnormal development of the Ts1Cje mouse brains.

Matched MeSH terms: Brain/physiology*

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