The Cuban Human Brain Mapping Project population based normative EEG, MRI, and Cognition dataset

AbstractThe Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 healthy participants (31.9 ± 9.3 years, age range 18–68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion included presence of disease or brain dysfunctions. The information made available for all participants comprises: high-density (64-120 channels) resting state electroencephalograms (EEG), magnetic resonance images (MRI), psychological tests (MMSE, Wechsler Adult Intelligence Scale -WAIS III, computerized reaction time tests using a go no-go paradigm), as well as general information (age, gender, education, ethnicity, handedness and weight). The EEG data contains recordings with at least 30 minutes duration including the following conditions: eyes closed, eyes open, hyperventilation and subsequent recovery. The MRI consisted in anatomical T1 and T2 as well as diffusion weighted (DWI) images acquired on a 1.5 Tesla system. The data is available for registered users on the LORIS database which is part of the MNI neuroinformatics ecosystem.

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The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset

Scientific Data ◽

10.1038/s41597-021-00829-7 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Pedro A. Valdes-Sosa ◽

Lidice Galan-Garcia ◽

Jorge Bosch-Bayard ◽

Maria L. Bringas-Vega ◽

Eduardo Aubert-Vazquez ◽

...

Keyword(s):

Human Brain ◽

Brain Mapping ◽

Middle Age ◽

Population Based ◽

Magnetic Resonance Images ◽

Human Brain Mapping ◽

Eyes Closed ◽

Eeg Data ◽

Eyes Open ◽

Age Range

AbstractThe Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18–68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64–120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca.

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Convergence of human brain mapping tools: Neuronavigated TMS Parameters and fMRI activity in the hand motor area

Human Brain Mapping ◽

10.1002/hbm.21272 ◽

2011 ◽

Vol 33 (5) ◽

pp. 1107-1123 ◽

Cited By ~ 34

Author(s):

Anna-Sophia Sarfeld ◽

Svenja Diekhoff ◽

Ling E. Wang ◽

Gianpiero Liuzzi ◽

Kamil Uludağ ◽

...

Keyword(s):

Human Brain ◽

Brain Mapping ◽

Motor Area ◽

Human Brain Mapping ◽

Hand Motor Area

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Adaptive Gaussian Markov random fields with applications in human brain mapping

Journal of the Royal Statistical Society Series C (Applied Statistics) ◽

10.1111/j.1467-9876.2007.00580.x ◽

2007 ◽

Vol 56 (3) ◽

pp. 327-345 ◽

Cited By ~ 21

Author(s):

A. Brezger ◽

L. Fahrmeir ◽

A. Hennerfeind

Keyword(s):

Human Brain ◽

Brain Mapping ◽

Random Fields ◽

Markov Random Fields ◽

Human Brain Mapping ◽

Gaussian Markov Random Fields ◽

Markov Random

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Astronomia nova to human brain mapping

Neural Networks ◽

10.1016/j.neunet.2006.09.007 ◽

2006 ◽

Vol 19 (9) ◽

pp. 1453-1454

Author(s):

Rik Vandenberghe

Keyword(s):

Human Brain ◽

Brain Mapping ◽

Human Brain Mapping

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Impact of Global Mean Normalization on Regional Glucose Metabolism in the Human Brain

Neural Plasticity ◽

10.1155/2018/6120925 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Kristian N. Mortensen ◽

Albert Gjedde ◽

Garth J. Thompson ◽

Peter Herman ◽

Maxime J. Parent ◽

...

Keyword(s):

Glucose Metabolism ◽

Human Brain ◽

Disease Biomarkers ◽

Eyes Closed ◽

Positron Emission ◽

Congenitally Blind ◽

Eyes Open ◽

Health And Disease ◽

The Impact ◽

Global Mean

Because the human brain consumes a disproportionate fraction of the resting body’s energy, positron emission tomography (PET) measurements of absolute glucose metabolism (CMRglc) can serve as disease biomarkers. Global mean normalization (GMN) of PET data reveals disease-based differences from healthy individuals as fractional changes across regions relative to a global mean. To assess the impact of GMN applied to metabolic data, we compared CMRglc with and without GMN in healthy awake volunteers with eyes closed (i.e., control) against specific physiological/clinical states, including healthy/awake with eyes open, healthy/awake but congenitally blind, healthy/sedated with anesthetics, and patients with disorders of consciousness. Without GMN, global CMRglc alterations compared to control were detected in all conditions except in congenitally blind where regional CMRglc variations were detected in the visual cortex. However, GMN introduced regional and bidirectional CMRglc changes at smaller fractions of the quantitative delocalized changes. While global information was lost with GMN, the quantitative approach (i.e., a validated method for quantitative baseline metabolic activity without GMN) not only preserved global CMRglc alterations induced by opening eyes, sedation, and varying consciousness but also detected regional CMRglc variations in the congenitally blind. These results caution the use of GMN upon PET-measured CMRglc data in health and disease.

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Z-Score EEG Biofeedback: Past, Present, and Future

Biofeedback ◽

10.5298/1081-5937-47.4.04 ◽

2019 ◽

Vol 47 (4) ◽

pp. 89-103

Author(s):

Robert W. Thatcher ◽

Joel F. Lubar ◽

J. Lucas Koberda

Keyword(s):

Human Brain ◽

Real Time ◽

Brain Mapping ◽

Three Dimensional ◽

Brain Network ◽

Eeg Biofeedback ◽

Reference Database ◽

Symptom Checklist ◽

Z Score ◽

Human Brain Mapping

Human electroencephalogram (EEG) biofeedback (neurofeedback) started in the 1940s using one EEG recording channel, then four channels in the 1990s, and in 2004, expanded to 19 channels using Low Resolution Electromagnetic Tomography (LORETA) of the microampere three-dimensional current sources of the EEG. In 2004–2006 the concept of a real-time comparison of the EEG to a healthy reference database was developed and tested using surface EEG z score neurofeedback based on a statistical bell curve called real-time z scores. The real-time or live normative reference database comparison was developed to help reduce the uncertainty of what threshold to select to activate a feedback signal and to unify all EEG measures to a single value (i.e., the distance from the mean of an age-matched reference sample). In 2009 LORETA z score neurofeedback further increased specificity by targeting brain network hubs referred to as Brodmann areas. A symptom checklist program to help link symptoms to dysregulation of brain networks based on fMRI and positron emission tomography (PET) and neurology was created in 2009. The symptom checklist and National Institutes of Health–based networks linking symptoms to brain networks grew out of the human brain mapping program started in 1990 that continues today. A goal is to increase specificity of EEG biofeedback by targeting brain network hubs and connections between hubs likely linked to the patient's symptoms. Developments first introduced in 2017 provide increased resolution of three-dimensional source localization with 12,700 voxels using swLORETA with the capacity to conduct cerebellar neurofeedback and neurofeedback of subcortical brain hubs such as the thalamus, amygdala, and habenula. Future applications of swLORETA z score neurofeedback represent another example of the transfer of knowledge gained by the human brain mapping initiatives to further aid in helping people with cognition problems as well as balance problems and parkinsonism. A brief review of the past, present, and future predictions of z score neurofeedback are discussed with special emphasis on new developments that point toward a bright and enlightened future in the field of EEG biofeedback.

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