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.

Neuro-Clinical Signatures of Language Impairments: A Theoretical Framework for Function-to-structure Mapping in Clinics

2020 ◽  
Vol 20 (9) ◽  
pp. 800-811 ◽  
Author(s):  
Ferath Kherif ◽  
Sandrine Muller
Keyword(s):  
Brain Mapping ◽  
Theoretical Framework ◽  
Brain Regions ◽  
Language Impairments ◽  
Structure Mapping ◽  
Language Functions ◽  
The Past ◽  
Language Recovery ◽  
The Brain ◽  
Bow Tie

In the past decades, neuroscientists and clinicians have collected a considerable amount of data and drastically increased our knowledge about the mapping of language in the brain. The emerging picture from the accumulated knowledge is that there are complex and combinatorial relationships between language functions and anatomical brain regions. Understanding the underlying principles of this complex mapping is of paramount importance for the identification of the brain signature of language and Neuro-Clinical signatures that explain language impairments and predict language recovery after stroke. We review recent attempts to addresses this question of language-brain mapping. We introduce the different concepts of mapping (from diffeomorphic one-to-one mapping to many-to-many mapping). We build those different forms of mapping to derive a theoretical framework where the current principles of brain architectures including redundancy, degeneracy, pluri-potentiality and bow-tie network are described.

scholarly journals Electroencephalogram Brain Mapping for revealing the emotional changes over the brain regions using Entropy biomarker

2021 ◽  
Author(s):  
Noor Al-Qazzaz ◽  
Mohannad Sabir ◽  
Sawal Ali ◽  
Siti Anom Ahmad ◽  
Karl Grammer
Keyword(s):  
Brain Mapping ◽  
Brain Regions ◽  
Emotional Changes ◽  
The Brain

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 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 Ultra-high-order ICA: fine overlapping functional parcellations and spatiotemporal reconfiguration

2020 ◽  
Author(s):  
Armin Iraji ◽  
Zening Fu ◽  
Thomas DeRamus ◽  
Shile Qi ◽  
Srinivas Rachakonda ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Mapping ◽  
Spatial Variations ◽  
High Order ◽  
Human Brain Mapping ◽  
Functional Analyses ◽  
The Brain ◽  
Over Time

AbstractOur recent findings show that functional organizations evolve spatially over time, highlighting the importance of considering within-subject spatial variations and dynamic functional parcellations in brain functional analyses. Meanwhile, a considerable level of multi-functionality suggests the need for overlapping brain parcellations. In this work, we used ultra-high-order ICA to identify fine overlapping functional dynamic parcellations of the brain. The preliminary result of this work was presented at the organization for human brain mapping workshop (OHBM 2019)1.

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 Evaluating brain parcellations using the distance controlled boundary coefficient

2021 ◽  
Author(s):  
Da Zhi ◽  
Maedbh King ◽  
Joern Diedrichsen
Keyword(s):  
Brain Mapping ◽  
Predictive Ability ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Anatomical Landmarks ◽  
Functional Brain Mapping ◽  
Brain Data ◽  
Homogeneous Regions ◽  
New Criterion ◽  
The Brain

An important goal of human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic data, structural or functional Magnetic Resonance Imaging (fMRI). The intrinsic smoothness of the brain data, however, poses a problem for current methods seeking to compare different parcellations to each other. For example, criteria that simply compare within-parcel to between-parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the Distance Controlled Boundary Coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate whether existing parcellations of the human neocortex can predict functional boundaries on a rich multi-domain task battery. We find that common anatomical parcellations do not perform better than chance, suggesting that task-based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting-state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi-modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define functionally and anatomically homogeneous brain regions.

scholarly journals The Semantics of Adjective Noun Phrases in the Human Brain

2016 ◽  
Author(s):  
Alona Fyshe ◽  
Gustavo Sudre ◽  
Leila Wehbe ◽  
Nicole Rafidi ◽  
Tom M. Mitchell
Keyword(s):  
Human Brain ◽  
Information Flow ◽  
Semantic Information ◽  
Semantic Representation ◽  
Noun Phrases ◽  
Brain Regions ◽  
Higher Order ◽  
Neural Representation ◽  
The Brain ◽  
Over Time

AbstractAs a person reads, the brain performs complex operations to create higher order semantic representations from individual words. While these steps are effortless for competent readers, we are only beginning to understand how the brain performs these actions. Here, we explore semantic composition using magnetoencephalography (MEG) recordings of people reading adjective-noun phrases presented one word at a time. We track the neural representation of semantic information over time, through different brain regions. Our results reveal two novel findings: 1) a neural representation of the adjective is present during noun presentation, but this neural representation is different from that observed during adjective presentation 2) the neural representation of adjective semantics observed during adjective reading is reactivated after phrase reading, with remarkable consistency. We also note that while the semantic representation of the adjective during the reading of the adjective is very distributed, the later representations are concentrated largely to temporal and frontal areas previously associated with composition. Taken together, these results paint a picture of information flow in the brain as phrases are read and understood.

scholarly journals Characterization of the brain functional architecture of psychostimulant withdrawal using single-cell whole brain imaging

2019 ◽  
Author(s):  
Adam Kimbrough ◽  
Lauren C. Smith ◽  
Marsida Kallupi ◽  
Sierra Simpson ◽  
Andres Collazo ◽  
...  
Keyword(s):  
Single Cell ◽  
Brain Imaging ◽  
Drugs Of Abuse ◽  
Brain Regions ◽  
Functional Architecture ◽  
Whole Brain ◽  
Similarities And Differences ◽  
Drug Dependent ◽  
The Brain

AbstractNumerous brain regions have been identified as contributing to addiction-like behaviors, but unclear is the way in which these brain regions as a whole lead to addiction. The search for a final common brain pathway that is involved in addiction remains elusive. To address this question, we used male C57BL/6J mice and performed single-cell whole-brain imaging of neural activity during withdrawal from cocaine, methamphetamine, and nicotine. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical- to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway that leads to addiction.Significance StatementA key aspect of treating drug abuse is understanding similarities and differences of how drugs of abuse affect the brain. In the present study we examined how the brain is altered during withdrawal from psychostimulants. We found that each drug produced a unique pattern of activity in the brain, but that brains in withdrawal from cocaine and methamphetamine shared similar features. Interestingly, we found the major common link between withdrawal from all psychostimulants, when compared to controls, was a shift in the broad organization of the brain in the form of reduced modularity. Reduced modularity has been shown in several brain disorders, including traumatic brain injury, and dementia, and may be the common link between drugs of abuse.

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