Charles-Francois Vincent Latchoumane
My dream is to have an impact on the general public through neuroscience. I Believe that we all deserve to understand and use our brain in the best way possible. Our lives and our dreams, all depend on what we can do, who we are and who we want to become in our own heads. I would define my success through how many people I can help, this is my new mission.
Field of Interest:
Neuroscience, Computational Neuroscience
Disease Management and Diagnosis
Signal Processing and Engineering
Brain Computer Interface and Neuromodulation
Diploma:
Ph.D. candidate, Bio and Brain Engineering (Neuroscience), 2006~present
M.S. Physics (Biotechnologies Instrumentation), 2006, ENSPG/INPG, France
M.S. Bio and Brain Engineering (Neuroscience), 2006, KAIST, South Korea
B.S. Physics, 2004, ENSPG/INPG, France
Supervisors: Heesup Shin
Address: KIST, Center for Neural Science
L7224, seongbuk-gu haweolgok-dong
39-1
136-130
Field of Interest:
Neuroscience, Computational Neuroscience
Disease Management and Diagnosis
Signal Processing and Engineering
Brain Computer Interface and Neuromodulation
Diploma:
Ph.D. candidate, Bio and Brain Engineering (Neuroscience), 2006~present
M.S. Physics (Biotechnologies Instrumentation), 2006, ENSPG/INPG, France
M.S. Bio and Brain Engineering (Neuroscience), 2006, KAIST, South Korea
B.S. Physics, 2004, ENSPG/INPG, France
Supervisors: Heesup Shin
Address: KIST, Center for Neural Science
L7224, seongbuk-gu haweolgok-dong
39-1
136-130
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Books by Charles-Francois Vincent Latchoumane
In a divide and conquer scheme, we obtained a classification accuracy of 74.7% comparing the control subjects to the demented subjects, and we obtained a classification accuracy of 75.6% comparing MCI subjects to AD patients. This approach combined the multilinear interaction within the tensor formed by subjects X frequency power X regions and provided an interesting interpretation and characterization of Alzheimer’s disease in the early stages from a simple set of features. The multiway modeling of EEG recordings applied to the characterization and classification of Alzheimer’s disease
patients in the early stages has not been employed as yet. Even though the classification results are modest compared with the available literature, this method could help extract more interesting features as well as summarize information for
classification or diagnosis at a higher level than subject-by-subject EEG analysis. This method, if combined with other features, could reveal itself to be very promising for diagnosing Alzheimer’s patients in the early stages. Moreover, it can be easily generalized as well as improved by numerous linear and nonlinear features of EEGs.
Papers by Charles-Francois Vincent Latchoumane
graphics, and other representations, to provide real-time information on ongoing
waves and patterns in the brain. Here we present various forms of neurofeedback,
including sonification, sonification in combination with visualization, and at last,
immersive neurofeedback, where auditory and visual feedback is provided in a
multi-sided immersive environment in which participants are completely surrounded by virtual imagery and 3D sound. Neural feedback may potentially
improve the user’s (or patient’s) ability to control brain activity, the diagnosis of
medical conditions, and the rehabilitation of neurological or psychiatric disorders.
Several psychological and medical studies have confirmed that virtual immersive
activity is enjoyable, stimulating, and can have a healing effect. As an illustration,
neurofeedback is generated from electroencephalograms (EEG) of Alzheimer’s
disease (AD) patients and healthy subjects. The auditory, visual, and immersive
representations of Alzheimer’s EEG differ substantially from healthy EEG, potentially
yielding novel diagnostic tools. Moreover, such alternative representations of
AD EEG are natural and intuitive, and hence easily accessible to laymen
(AD patients and family members)
(DNA) to the resting EEGs of patients with attentiondeficit/hyperactivity
disorder (AD/HD). We aimed to assess and
characterize AD/HD using features based on the local and global
duration of dynamical microstate. We hypothesized that AD/HD
patients would have difficulties in maintaining stable cognitive
states (e.g., attention deficit and impulsivity) and that they would
thus exhibit EEGs with temporal dynamics distinct from normal
controls, i.e., rapidly and frequently changing dynamics. To test
this hypothesis, we recorded EEGs from 12 adolescent subjects
with AD/HD and 11 age-matched healthy subjects in the resting
state with eyes closed and eyes open. We found that AD/HD patients
exhibited significantly faster changes in dynamics than controls in
the right temporal region during the eyes closed condition, but
slower changes in dynamics in the frontal region during the eyes
open condition. AD/HD patients exhibited a disruption in the rate
of change of dynamics in the frontotemporal region at rest, probably
due to executive and attention processes. We suggest that the
DNA using complementary local and global features based on the
duration of dynamical microstates could be a useful tool for the
clinical diagnosis of subjects with AD/HD.
activation of T-type Ca2+ channels in the thalamic reticular nucleus
(TRN). TRN bursts are believed to be critical for generation and
maintenance of thalamocortical oscillations, leading to the spikeand-wave
discharges (SWDs), which are the hallmarks of absence
seizures. We observed that the RBDs were completely abolished,
whereas tonic firing was significantly increased, in TRN neurons
from mice in which the gene for the T-type Ca2+ channel, CaV3.3,
was deleted (CaV3.3−/−). Contrary to expectations, there was an
increased susceptibility to drug-induced SWDs both in CaV3.3−/−
mice and in mice in which the CaV3.3 gene was silenced predominantly
in the TRN. CaV3.3−/− mice also showed enhanced inhibitory
synaptic drive onto TC neurons. Finally, a double knockout of
both CaV3.3 and CaV3.2, which showed complete elimination of
burst firing and RBDs in TRN neurons, also displayed enhanced
drug-induced SWDs and absence seizures. On the other hand,
tonic firing in the TRN was increased in these mice, suggesting
that increased tonic firing in the TRN may be sufficient for druginduced
SWD generation in the absence of burst firing. These
results call into question the role of burst firing in TRN neurons
in the genesis of SWDs, calling for a rethinking of the mechanism
for absence seizure induction.
dynamics, with abrupt micro- and macrostate transitions during its
information processing. Detecting and characterizing these transitions
in dynamical states of the brain is a critical issue in the
field of neuroscience and psychiatry. In the current study, a novel
method is proposed to quantify brain macrostates (e.g., sleep stages
or cognitive states) from shifts of dynamical microstates or dynamical
nonstationarity. A “dynamical microstate” is a temporal unit
of the information processing in the brain with fixed dynamical
parameters and specific spatial distribution. In this proposed approach,
a phase-space-based dynamical dissimilarity map (DDM)
is used to detect transitions between dynamically stationary microstates
in the time series, and Tsallis time-dependent entropy is
applied to quantify dynamical patterns of transitions in the DDM.
We demonstrate that the DDM successfully detects transitions between
microstates of different temporal dynamics in the simulated
physiological time series against high levels of noise. Based on the
assumption of nonlinear, deterministic brain dynamics, we also
demonstrate that dynamical nonstationarity analysis is useful to
quantify brain macrostates (sleep stages I, II, III, IV, and rapid eye
movement (REM) sleep) from sleep EEGs with an overall accuracy
of 77%. We suggest that dynamical nonstationarity is a useful tool
to quantify macroscopic mental states (statistical integration) of
the brain using dynamical transitions at the microscopic scale in
physiological data.
The brain shows complex, nonstationarity temporal dynamics, with abrupt micro- and macrostate transitions during its information processing. Detecting and characterizing these transitions in dynamical states of the brain is a critical issue in the field of neuroscience and Psychiatry. In the current study, a novel method is proposed to quantify brain macrostates (e.g. sleep stages or cognitive states) from shifts of dynamical microstates or dynamical nonstationarity. A ‘dynamical microstate’ is a temporal unit of the information processing in the brain with fixed dynamical parameters and specific spatial distribution. In this proposed approach, a phase-space-based dynamical dissimilarity map (DDM) is used to detect transitions between dynamically stationary microstates in the time series, and Tsallis time-dependent entropy is applied to quantify dynamical patterns of transitions in the DDM. We demonstrate that the DDM successfully detects transitions between microstates of different temporal dynamics in the simulated physiological time series against high levels of noise. Based on the assumption of nonlinear, deterministic brain dynamics, we also demonstrate that dynamical nonstationarity analysis is useful to quantify brain macrostates (Sleep stages I, II, III, IV, and rapid eye movement (REM) sleep) from sleep EEGs with an overall accuracy of 77%. We suggest that dynamical nonstationarity is a useful tool to quantify macroscopic mental states (statistical integration) of the brain using dynamical transitions at the microscopic scale in physiological data.
In a divide and conquer scheme, we obtained a classification
accuracy of 74.7% comparing the control subjects to the
demented subjects, and we obtained a classification accuracy of 75.6% comparing MCI subjects to AD patients. This approach combined the multilinear interaction within the tensor formed by subjects X frequency power X regions and provided an interesting interpretation and characterization of Alzheimer’s disease in the early stages from a simple set of features. The multiway modeling of EEG recordings applied to the characterization and classification of Alzheimer’s disease
patients in the early stages has not been employed as yet. Even though the classification results are modest compared with the available literature, this method could help extract more interesting features as well as summarize information for
classification or diagnosis at a higher level than subject-by-subject EEG analysis. This method, if combined with other features, could reveal itself to be very promising for diagnosing Alzheimer’s patients in the early stages. Moreover, it can be easily generalized as well as improved by numerous linear and nonlinear features of EEGs.
sec. long) that employed abstractive visuals and non-lyrical musical expressions? If yes, would there be any common thread in audience responses to these purpose-driven new creations? In this study, under a hypothesis that it is possible to create certain emotion/mood inducing multi-modal contents, we first researched various psychology (and/or therapy) fields (e.g., music, colors, images, and motiongraphics) for guidelines to design three specific types of positive emotion elicitations (i.e., Relaxation, Happy, and Vigorous), and produced audio-visual contents based on the learned expressive attributes. Then we investigated the response of 12 subjects (6 males and 6 females, mean age 22 year old ) on their EEG power differences between rest and watching movie sections, alpha asymmetry, cognitive performances during visual congruent continuous performance tasks (cCPT, attentional task), and self-evaluation questionnaires. We concluded that emotional/mood induction using multi-modal contents could bring out changes in attention, visible from a behavioral study, however milder in the electrophysiological response.
In a divide and conquer scheme, we obtained a classification accuracy of 74.7% comparing the control subjects to the demented subjects, and we obtained a classification accuracy of 75.6% comparing MCI subjects to AD patients. This approach combined the multilinear interaction within the tensor formed by subjects X frequency power X regions and provided an interesting interpretation and characterization of Alzheimer’s disease in the early stages from a simple set of features. The multiway modeling of EEG recordings applied to the characterization and classification of Alzheimer’s disease
patients in the early stages has not been employed as yet. Even though the classification results are modest compared with the available literature, this method could help extract more interesting features as well as summarize information for
classification or diagnosis at a higher level than subject-by-subject EEG analysis. This method, if combined with other features, could reveal itself to be very promising for diagnosing Alzheimer’s patients in the early stages. Moreover, it can be easily generalized as well as improved by numerous linear and nonlinear features of EEGs.
graphics, and other representations, to provide real-time information on ongoing
waves and patterns in the brain. Here we present various forms of neurofeedback,
including sonification, sonification in combination with visualization, and at last,
immersive neurofeedback, where auditory and visual feedback is provided in a
multi-sided immersive environment in which participants are completely surrounded by virtual imagery and 3D sound. Neural feedback may potentially
improve the user’s (or patient’s) ability to control brain activity, the diagnosis of
medical conditions, and the rehabilitation of neurological or psychiatric disorders.
Several psychological and medical studies have confirmed that virtual immersive
activity is enjoyable, stimulating, and can have a healing effect. As an illustration,
neurofeedback is generated from electroencephalograms (EEG) of Alzheimer’s
disease (AD) patients and healthy subjects. The auditory, visual, and immersive
representations of Alzheimer’s EEG differ substantially from healthy EEG, potentially
yielding novel diagnostic tools. Moreover, such alternative representations of
AD EEG are natural and intuitive, and hence easily accessible to laymen
(AD patients and family members)
(DNA) to the resting EEGs of patients with attentiondeficit/hyperactivity
disorder (AD/HD). We aimed to assess and
characterize AD/HD using features based on the local and global
duration of dynamical microstate. We hypothesized that AD/HD
patients would have difficulties in maintaining stable cognitive
states (e.g., attention deficit and impulsivity) and that they would
thus exhibit EEGs with temporal dynamics distinct from normal
controls, i.e., rapidly and frequently changing dynamics. To test
this hypothesis, we recorded EEGs from 12 adolescent subjects
with AD/HD and 11 age-matched healthy subjects in the resting
state with eyes closed and eyes open. We found that AD/HD patients
exhibited significantly faster changes in dynamics than controls in
the right temporal region during the eyes closed condition, but
slower changes in dynamics in the frontal region during the eyes
open condition. AD/HD patients exhibited a disruption in the rate
of change of dynamics in the frontotemporal region at rest, probably
due to executive and attention processes. We suggest that the
DNA using complementary local and global features based on the
duration of dynamical microstates could be a useful tool for the
clinical diagnosis of subjects with AD/HD.
activation of T-type Ca2+ channels in the thalamic reticular nucleus
(TRN). TRN bursts are believed to be critical for generation and
maintenance of thalamocortical oscillations, leading to the spikeand-wave
discharges (SWDs), which are the hallmarks of absence
seizures. We observed that the RBDs were completely abolished,
whereas tonic firing was significantly increased, in TRN neurons
from mice in which the gene for the T-type Ca2+ channel, CaV3.3,
was deleted (CaV3.3−/−). Contrary to expectations, there was an
increased susceptibility to drug-induced SWDs both in CaV3.3−/−
mice and in mice in which the CaV3.3 gene was silenced predominantly
in the TRN. CaV3.3−/− mice also showed enhanced inhibitory
synaptic drive onto TC neurons. Finally, a double knockout of
both CaV3.3 and CaV3.2, which showed complete elimination of
burst firing and RBDs in TRN neurons, also displayed enhanced
drug-induced SWDs and absence seizures. On the other hand,
tonic firing in the TRN was increased in these mice, suggesting
that increased tonic firing in the TRN may be sufficient for druginduced
SWD generation in the absence of burst firing. These
results call into question the role of burst firing in TRN neurons
in the genesis of SWDs, calling for a rethinking of the mechanism
for absence seizure induction.
dynamics, with abrupt micro- and macrostate transitions during its
information processing. Detecting and characterizing these transitions
in dynamical states of the brain is a critical issue in the
field of neuroscience and psychiatry. In the current study, a novel
method is proposed to quantify brain macrostates (e.g., sleep stages
or cognitive states) from shifts of dynamical microstates or dynamical
nonstationarity. A “dynamical microstate” is a temporal unit
of the information processing in the brain with fixed dynamical
parameters and specific spatial distribution. In this proposed approach,
a phase-space-based dynamical dissimilarity map (DDM)
is used to detect transitions between dynamically stationary microstates
in the time series, and Tsallis time-dependent entropy is
applied to quantify dynamical patterns of transitions in the DDM.
We demonstrate that the DDM successfully detects transitions between
microstates of different temporal dynamics in the simulated
physiological time series against high levels of noise. Based on the
assumption of nonlinear, deterministic brain dynamics, we also
demonstrate that dynamical nonstationarity analysis is useful to
quantify brain macrostates (sleep stages I, II, III, IV, and rapid eye
movement (REM) sleep) from sleep EEGs with an overall accuracy
of 77%. We suggest that dynamical nonstationarity is a useful tool
to quantify macroscopic mental states (statistical integration) of
the brain using dynamical transitions at the microscopic scale in
physiological data.
The brain shows complex, nonstationarity temporal dynamics, with abrupt micro- and macrostate transitions during its information processing. Detecting and characterizing these transitions in dynamical states of the brain is a critical issue in the field of neuroscience and Psychiatry. In the current study, a novel method is proposed to quantify brain macrostates (e.g. sleep stages or cognitive states) from shifts of dynamical microstates or dynamical nonstationarity. A ‘dynamical microstate’ is a temporal unit of the information processing in the brain with fixed dynamical parameters and specific spatial distribution. In this proposed approach, a phase-space-based dynamical dissimilarity map (DDM) is used to detect transitions between dynamically stationary microstates in the time series, and Tsallis time-dependent entropy is applied to quantify dynamical patterns of transitions in the DDM. We demonstrate that the DDM successfully detects transitions between microstates of different temporal dynamics in the simulated physiological time series against high levels of noise. Based on the assumption of nonlinear, deterministic brain dynamics, we also demonstrate that dynamical nonstationarity analysis is useful to quantify brain macrostates (Sleep stages I, II, III, IV, and rapid eye movement (REM) sleep) from sleep EEGs with an overall accuracy of 77%. We suggest that dynamical nonstationarity is a useful tool to quantify macroscopic mental states (statistical integration) of the brain using dynamical transitions at the microscopic scale in physiological data.
In a divide and conquer scheme, we obtained a classification
accuracy of 74.7% comparing the control subjects to the
demented subjects, and we obtained a classification accuracy of 75.6% comparing MCI subjects to AD patients. This approach combined the multilinear interaction within the tensor formed by subjects X frequency power X regions and provided an interesting interpretation and characterization of Alzheimer’s disease in the early stages from a simple set of features. The multiway modeling of EEG recordings applied to the characterization and classification of Alzheimer’s disease
patients in the early stages has not been employed as yet. Even though the classification results are modest compared with the available literature, this method could help extract more interesting features as well as summarize information for
classification or diagnosis at a higher level than subject-by-subject EEG analysis. This method, if combined with other features, could reveal itself to be very promising for diagnosing Alzheimer’s patients in the early stages. Moreover, it can be easily generalized as well as improved by numerous linear and nonlinear features of EEGs.
sec. long) that employed abstractive visuals and non-lyrical musical expressions? If yes, would there be any common thread in audience responses to these purpose-driven new creations? In this study, under a hypothesis that it is possible to create certain emotion/mood inducing multi-modal contents, we first researched various psychology (and/or therapy) fields (e.g., music, colors, images, and motiongraphics) for guidelines to design three specific types of positive emotion elicitations (i.e., Relaxation, Happy, and Vigorous), and produced audio-visual contents based on the learned expressive attributes. Then we investigated the response of 12 subjects (6 males and 6 females, mean age 22 year old ) on their EEG power differences between rest and watching movie sections, alpha asymmetry, cognitive performances during visual congruent continuous performance tasks (cCPT, attentional task), and self-evaluation questionnaires. We concluded that emotional/mood induction using multi-modal contents could bring out changes in attention, visible from a behavioral study, however milder in the electrophysiological response.