scholarly journals Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1615 ◽  
Author(s):  
Indranath Chatterjee
Keyword(s):  
Time Series ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Medical Intervention ◽  
Fmri Data ◽  
Mean Deviation ◽  
Functional Changes ◽  
Statistical Measures ◽  
The Brain ◽  
Over Time

Background: Schizophrenia is a serious mental illness affecting different regions of the brain, which causes symptoms such as hallucinations and delusions. Functional magnetic resonance imaging (fMRI) is the most popular technique to study the functional activation patterns of the brain. The fMRI data is four-dimensional, composed of 3D brain images over time. Each voxel of the 3D brain volume is associated with a time series of signal intensity values. This study aimed to identify the distinct voxels from time-series fMRI data that show high functional activation during a task. Methods: In this study, a novel mean-deviation based approach was applied to time-series fMRI data of 34 schizophrenia patients and 34 healthy subjects. The statistical measures such as mean and median were used to find the functional changes in each voxel over time. The voxels that show significant changes for each subject were selected and thus used as the feature set during the classification of schizophrenia patients and healthy controls. Results: The proposed approach identifies a set of relevant voxels that are used to distinguish between healthy and schizophrenia subjects with high classification accuracy. The study shows functional changes in brain regions such as superior frontal gyrus, cuneus, medial frontal gyrus, middle occipital gyrus, and superior temporal gyrus. Conclusions: This work describes a simple yet novel feature selection algorithm for time-series fMRI data to identify the activated brain voxels that are generally affected in schizophrenia. The brain regions identified in this study may further help clinicians to understand the illness for better medical intervention. It may be possible to explore the approach to fMRI data of other psychological disorders.

scholarly journals Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1615 ◽  
Author(s):  
Indranath Chatterjee
Keyword(s):  
Time Series ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Medical Intervention ◽  
Fmri Data ◽  
Mean Deviation ◽  
Functional Changes ◽  
Statistical Measures ◽  
The Brain ◽  
Over Time

Background: Schizophrenia is a serious mental illness affecting different regions of the brain, which causes symptoms such as hallucinations and delusions. Functional magnetic resonance imaging (fMRI) is the most popular technique to study the functional activation patterns of the brain. The fMRI data is four-dimensional, composed of 3D brain images over time. Each voxel of the 3D brain volume is associated with a time series of signal intensity values. This study aimed to identify the distinct voxels from time-series fMRI data that show high functional activation during a task. Methods: In this study, a novel mean-deviation based approach was applied to time-series fMRI data of 34 schizophrenia patients and 34 healthy subjects. The statistical measures such as mean and median were used to find the functional changes in each voxel over time. The voxels that show significant changes for each subject were selected and thus used as the feature set during the classification of schizophrenia patients and healthy controls. Results: The proposed approach identifies a set of relevant voxels that are used to distinguish between healthy and schizophrenia subjects with high classification accuracy. The study shows functional changes in brain regions such as superior frontal gyrus, cuneus, medial frontal gyrus, middle occipital gyrus, and superior temporal gyrus. Conclusions: This work describes a simple yet novel feature selection algorithm for time-series fMRI data to identify the activated brain voxels that are generally affected in schizophrenia. The brain regions identified in this study may further help clinicians to understand the illness for better medical intervention. It may be possible to explore the approach to fMRI data of other psychological disorders.

scholarly journals Identification of brain regions associated with working memory deficit in schizophrenia

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 124 ◽  
Author(s):  
Indranath Chatterjee ◽  
Virendra Kumar ◽  
Sahil Sharma ◽  
Divyanshi Dhingra ◽  
Bharti Rana ◽  
...  
Keyword(s):  
Working Memory ◽  
Clinical Investigation ◽  
Brain Regions ◽  
Medical Intervention ◽  
Memory Deficit ◽  
Psychological Disorder ◽  
Imaging Data ◽  
Functional Changes ◽  
Statistical Measures ◽  
The Brain

Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory. Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected. Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients. Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

Neural Mechanisms of Memory Enhancement and Impairment Induced by Visual Statistical Learning

2020 ◽  
Vol 32 (9) ◽  
pp. 1749-1763
Author(s):  
Sachio Otsuka ◽  
Jun Saiki
Keyword(s):  
Statistical Learning ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Neural Mechanisms ◽  
Fmri Data ◽  
Precentral Gyrus ◽  
Memory Enhancement ◽  
Visual Statistical Learning ◽  
Mechanisms Of Memory ◽  
The Brain

Prior research has reported that the medial temporal, parietal, and frontal brain regions are associated with visual statistical learning (VSL). However, the neural mechanisms involved in both memory enhancement and impairment induced by VSL remain unknown. In this study, we examined this issue using event-related fMRI. fMRI data from the familiarization scan showed a difference in the activation level of the superior frontal gyrus (SFG) between structured triplets, where three objects appeared in the same order, and pseudorandom triplets. More importantly, the precentral gyrus and paracentral lobule responded more strongly to Old Turkic letters inserted into the structured triplets than to those inserted into the random triplets, at the end of the familiarization scan. Furthermore, fMRI data from the recognition memory test scan, where participants were asked to decide whether the objects or letters shown were old (presented during familiarization scan) or new, indicated that the middle frontal gyrus and SFG responded more strongly to objects from the structured triplets than to those from the random triplets, which overlapped with the brain regions associated with VSL. In contrast, the response of the lingual gyrus, superior temporal gyrus, and cuneus was weaker to letters inserted into the structured triplets than to those inserted into the random triplets, which did not overlap with the brain regions associated with observing the letters during the familiarization scan. These findings suggest that different brain regions are involved in memory enhancement and impairment induced by VSL.

scholarly journals Slow Cortical Waves via Cyclicity

2021 ◽  
Author(s):  
Ivan Abraham ◽  
Bahar Shahsavarani ◽  
Ben Zimmerman ◽  
Fatima Husain ◽  
yuliy baryshnikov
Keyword(s):  
Time Series ◽  
Dynamic Structure ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fine Grained ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
The Brain ◽  
Cortical Waves ◽  
Temporal Ordering

Fine-grained information about dynamic structure of cortical networks is crucial in unpacking brain function. Here,we introduced a novel analytical method to characterize the dynamic interaction between distant brain regions,based on cyclicity analysis, and applied it to data from the Human Connectome Project. Resting-state fMRI time series are aperiodic and, hence, lack a base frequency. Cyclicity analysis, which is time-reparametrization invariant, is effective in recovering dynamic temporal ordering of such time series along a circular trajectory without assuming any time scale. Our analysis detected the propagation of slow cortical waves across thebrain with consistent shifts in lead-lag relationships between specific brain regions. We also observed short bursts of strong temporal ordering that dominated overall lead-lag relationships between pairs of regions in the brain, which were modulated by tasks. Our results suggest the possible role played by slow waves of ordered information between brain regions that underlie emergent cognitive function.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

Plastic Maps: The New Brain Cartographies of the 21th-Century Neurosciences

Nuncius ◽  
2017 ◽  
Vol 32 (2) ◽  
pp. 472-500
Author(s):  
Carmela Morabito
Keyword(s):  
Brain Mapping ◽  
Brain Regions ◽  
Individual Experience ◽  
Clinical Use ◽  
Functional Architecture ◽  
Early 19Th Century ◽  
New Models ◽  
Local Properties ◽  
The Brain ◽  
Over Time

Ever since the phrenological heads of the early 19th century, maps have translated into images our ideas, theories and models of the brain, making this organ at one and the same time scientific object and representation. Brain maps have always served as gateways for navigating and visualizing neuroscientific knowledge, and over time many different maps have been produced – firstly as tools to “read” and analyse the cerebral territory, then as instruments to produce new models of the brain. Over the last 150 years brain cartography has evolved from a way of identifying brain regions and localizing them for clinical use to an anatomical framework onto which information about local properties and functions can be integrated to provide a view of the brain’s structural and functional architecture. In this paper a historical and epistemological consideration of the topic is offered as a contribution to the understanding of contemporary brain mapping, based on the assumption that the brain continuously rewires itself in relation to individual experience.

scholarly journals 3265 Analysis of High-Dimensional Patient Data in Characterizing Alzheimer’s Disease Progression

2019 ◽  
Vol 3 (s1) ◽  
pp. 3-3
Author(s):  
Daniel Baer ◽  
Andrew B. Lawson ◽  
Brandon Vaughan ◽  
Jane E. Joseph
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Cognitive Decline ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Eigenvector Centrality ◽  
Research Hypothesis ◽  
The Brain ◽  
Over Time

OBJECTIVES/SPECIFIC AIMS: Our research hypothesis is that resting state fMRI (rsfMRI) data can be used to identify regions of the brain which are associated with cognitive decline in patients – thereby providing a tool by which to characterize AD progression in patients. METHODS/STUDY POPULATION: We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to analyze Mini-Mental State Examination (MMSE) questionnaire scores from 14 patients diagnosed with AD at two measurement occasions. RsfMRI data was available at the first of these occasions for these patients. These rsfMRI data were summarized into 264 node-based graph theory measures of clustering coefficient and eigenvector centrality. To address our research hypothesis, we modeled changes in patient MMSE scores over time as a function of these rsfMRI data, controlling for relevant confounding factors. This model accounted for the high-dimensionality of our predictor data, the longitudinal nature of the outcome, and our desire to identify a subset of regions in the brain most associated with the MMSE outcome. RESULTS/ANTICIPATED RESULTS: The use of either the clustering coefficient or eigenvector centrality rsfMRI predictors in modeling MMSE scores for patients over time resulted in the identification of different subsets of brain regions associated with cognitive decline. This suggests that these predictors capture different information on patient propensity for cognitive decline. Further work is warranted to validate these results on a larger sample of ADNI patients. DISCUSSION/SIGNIFICANCE OF IMPACT: We conclude that different rsfMRI graph theory measures capture different aspects of cognitive function and decline in patients, which could be a future consideration in clinical practice.

scholarly journals Multi-dimensional connectivity: a conceptual and mathematical review

2020 ◽  
Author(s):  
Hamed Nili ◽  
Alessio Basti ◽  
Olaf Hauk ◽  
Laura Marzetti ◽  
Richard Henson
Keyword(s):  
Time Series ◽  
Region Of Interest ◽  
Principal Component ◽  
Point Of View ◽  
Fmri Data ◽  
Multiple Time ◽  
Multiple Time Series ◽  
One Dimensional ◽  
Mathematical Review ◽  
The Brain

The estimation of functional connectivity between regions of the brain, for example based on statistical dependencies between the time series of activity in each region, has become increasingly important in neuroimaging. Typically, multiple time series (e.g. from each voxel in fMRI data) are first reduced to a single time series that summarises the activity in a region of interest, e.g. by averaging across voxels or by taking the first principal component; an approach we call one-dimensional connectivity. However, this summary approach ignores potential multi-dimensional connectivity between two regions, and a number of recent methods have been proposed to capture such complex dependencies. Here we review the most common multi-dimensional connectivity methods, from an intuitive perspective, from a formal (mathematical) point of view, and through a number of simulated and real (fMRI and MEG) data examples that illustrate the strengths and weaknesses of each method. The paper is accompanied with both functions and scripts, which implement each method and reproduce all the examples.

scholarly journals Neuroimaging Studies of Acupuncture on Low Back Pain: A Systematic Review

2021 ◽  
Vol 15 ◽  
Author(s):  
Qiao Wen ◽  
Peihong Ma ◽  
Xiaohui Dong ◽  
Ruirui Sun ◽  
Lei Lan ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Brain Regions ◽  
Risk Of Bias ◽  
Anterior Cingulate ◽  
Low Back ◽  
Imaging Data ◽  
China National Knowledge Infrastructure ◽  
Functional Changes ◽  
The Brain

Objectives: This study was conducted in order to investigate the study design and main outcomes of acupuncture neuroimaging studies on low back pain (LBP).Methods: Neuroimaging studies of acupuncture on LBP were collected from three English databases such as PubMed and four Chinese databases such as China National Knowledge Infrastructure (CNKI) from inception to December 31, 2020. Study selection, data extraction, and assessment of risk of bias were performed independently by two investigators. The quality of studies was appraised with the Cochrane's risk of bias tools. Information on basic information, methodology, and brain imaging data were extracted.Results: The literature search returned 310 potentially eligible studies and 19 articles met inclusion criteria; 78.9% of studies chose manual acupuncture as the intervention, 89.5% of studies evaluated functional changes elicited by acupuncture, and 68.4% of studies used resting-state fMRI as imaging condition. The most frequently reported acupuncture-induced brain alterations of LBP patients were in the prefrontal cortex, insula, cerebellum, primary somatosensory cortex, and anterior cingulate cortex. There was a significant correlation between improved clinical outcomes and changes in the brain.Conclusions: The results suggested that improving abnormal structure and functional activities in the brain of the LBP patient is an important mechanism of acupuncture treatment for LBP. The brain regions involved in acupuncture analgesia for LBP were mainly located in the pain matrix, default mode network (DMN), salience network (SN), and descending pain modulatory system (DPMS). However, it was difficult to draw a generalized conclusion due to the heterogeneity of study designs. Further well-designed multimodal neuroimaging studies investigating the mechanism of acupuncture on LBP are warranted.

scholarly journals Detecting brain connectivity changes during seizures using Granger causality measures on scalp EEG

2015 ◽  
Author(s):  
Christos Koutlis
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Window ◽  
Brain Connectivity ◽  
Characteristic Curve ◽  
Multivariate Time Series ◽  
Brain Regions ◽  
Brain Network ◽  
Network Feature ◽  
The Brain

In this work the objective is to detect brain connectivity changes during epileptic seizures using methods of multivariate time series analysis on scalp multi-channel EEG. Different brain regions represented by the electrode positions interact in terms of Granger causality and these directed connections formulate the brain network at a certain time window. The numerous proposed network features are believed to capture the information of many network characteristics. The ability of a single network feature of the brain network to detect the transition of brain activity from preictal to ictal is examined. The connectivity of the brain is estimated by 13 Granger causality indices on 7 epochs from multivariate time series (19 channels per epoch) at 15 time windows of 20 seconds (5 min in total) before seizure and during the seizure. The characteristics of the networks are estimated by 379 network features. Finally, the discrimination task (preictal vs. ictal) for each network feature is evaluated by the area under receiver operating characteristic curve (AUROC).

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