Kozunov Vladimir
Senior research fellow
PhD in Psychology
Project:
Publications:
2024
5.
Fadeev, Kirill A.; Romero Reyes, Ilacai V.; Goiaeva, Dzerassa E.; Obukhova, Tatiana S.; Ovsiannikova, Tatiana M.; Prokofyev, Andrey O.; Rytikova, Anna M.; Novikov, Artem Y.; Kozunov, Vladimir V.; Stroganova, Tatiana A.; Orekhova, Elena V. (2024). Attenuated processing of vowels in the left temporal cortex predicts speech-in-noise perception deficit in children with autism. Journal of Neurodevelopmental Disorders, 16(1). https://doi.org/10.1186/s11689-024-09585-2
@article{Fadeev2024,
title = {Attenuated processing of vowels in the left temporal cortex predicts speech-in-noise perception deficit in children with autism},
author = {Kirill A. Fadeev and Romero Reyes, Ilacai V. and Dzerassa E. Goiaeva and Tatiana S. Obukhova and Tatiana M. Ovsiannikova and Andrey O. Prokofyev and Anna M. Rytikova and Artem Y. Novikov and Vladimir V. Kozunov and Tatiana A. Stroganova and Elena V. Orekhova},
url = {https://megmoscow.ru/wp-content/uploads/pubs/10.1186_s11689-024-09585-2.pdf},
doi = {10.1186/s11689-024-09585-2},
year = {2024},
date = {2024-12-06},
urldate = {2024-12-06},
journal = {Journal of Neurodevelopmental Disorders},
volume = {16},
number = {1},
publisher = {Springer Science and Business Media LLC},
abstract = {Background
Difficulties with speech-in-noise perception in autism spectrum disorders (ASD) may be associated with impaired analysis of speech sounds, such as vowels, which represent the fundamental phoneme constituents of human speech. Vowels elicit early (< 100 ms) sustained processing negativity (SPN) in the auditory cortex that reflects the detection of an acoustic pattern based on the presence of formant structure and/or periodic envelope information (f0) and its transformation into an auditory “object”.
Methods
We used magnetoencephalography (MEG) and individual brain models to investigate whether SPN is altered in children with ASD and whether this deficit is associated with impairment in their ability to perceive speech in the background of noise. MEG was recorded while boys with ASD and typically developing boys passively listened to sounds that differed in the presence/absence of f0 periodicity and formant structure. Word-in-noise perception was assessed in the separate psychoacoustic experiment using stationary and amplitude modulated noise with varying signal-to-noise ratio.
Results
SPN was present in both groups with similarly early onset. In children with ASD, SPN associated with processing formant structure was reduced predominantly in the cortical areas lateral to and medial to the primary auditory cortex, starting at ~ 150—200 ms after the stimulus onset. In the left hemisphere, this deficit correlated with impaired ability of children with ASD to recognize words in amplitude-modulated noise, but not in stationary noise.
Conclusions
These results suggest that perceptual grouping of vowel formants into phonemes is impaired in children with ASD and that, in the left hemisphere, this deficit contributes to their difficulties with speech perception in fluctuating background noise.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Difficulties with speech-in-noise perception in autism spectrum disorders (ASD) may be associated with impaired analysis of speech sounds, such as vowels, which represent the fundamental phoneme constituents of human speech. Vowels elicit early (< 100 ms) sustained processing negativity (SPN) in the auditory cortex that reflects the detection of an acoustic pattern based on the presence of formant structure and/or periodic envelope information (f0) and its transformation into an auditory “object”.
Methods
We used magnetoencephalography (MEG) and individual brain models to investigate whether SPN is altered in children with ASD and whether this deficit is associated with impairment in their ability to perceive speech in the background of noise. MEG was recorded while boys with ASD and typically developing boys passively listened to sounds that differed in the presence/absence of f0 periodicity and formant structure. Word-in-noise perception was assessed in the separate psychoacoustic experiment using stationary and amplitude modulated noise with varying signal-to-noise ratio.
Results
SPN was present in both groups with similarly early onset. In children with ASD, SPN associated with processing formant structure was reduced predominantly in the cortical areas lateral to and medial to the primary auditory cortex, starting at ~ 150—200 ms after the stimulus onset. In the left hemisphere, this deficit correlated with impaired ability of children with ASD to recognize words in amplitude-modulated noise, but not in stationary noise.
Conclusions
These results suggest that perceptual grouping of vowel formants into phonemes is impaired in children with ASD and that, in the left hemisphere, this deficit contributes to their difficulties with speech perception in fluctuating background noise.
Difficulties with speech-in-noise perception in autism spectrum disorders (ASD) may be associated with impaired analysis of speech sounds, such as vowels, which represent the fundamental phoneme constituents of human speech. Vowels elicit early (< 100 ms) sustained processing negativity (SPN) in the auditory cortex that reflects the detection of an acoustic pattern based on the presence of formant structure and/or periodic envelope information (f0) and its transformation into an auditory “object”.
Methods
We used magnetoencephalography (MEG) and individual brain models to investigate whether SPN is altered in children with ASD and whether this deficit is associated with impairment in their ability to perceive speech in the background of noise. MEG was recorded while boys with ASD and typically developing boys passively listened to sounds that differed in the presence/absence of f0 periodicity and formant structure. Word-in-noise perception was assessed in the separate psychoacoustic experiment using stationary and amplitude modulated noise with varying signal-to-noise ratio.
Results
SPN was present in both groups with similarly early onset. In children with ASD, SPN associated with processing formant structure was reduced predominantly in the cortical areas lateral to and medial to the primary auditory cortex, starting at ~ 150—200 ms after the stimulus onset. In the left hemisphere, this deficit correlated with impaired ability of children with ASD to recognize words in amplitude-modulated noise, but not in stationary noise.
Conclusions
These results suggest that perceptual grouping of vowel formants into phonemes is impaired in children with ASD and that, in the left hemisphere, this deficit contributes to their difficulties with speech perception in fluctuating background noise.
4.
Ossadtchi, Alexei; Semenkov, Ilia; Zhuravleva, Anna; Kozunov, Vladimir; Serikov, Oleg; Voloshina, Ekaterina (2024). Representational dissimilarity component analysis (ReDisCA). NeuroImage, 301, 120868. https://doi.org/10.1016/j.neuroimage.2024.120868
@article{OSSADTCHI2024120868,
title = {Representational dissimilarity component analysis (ReDisCA)},
author = {Alexei Ossadtchi and Ilia Semenkov and Anna Zhuravleva and Vladimir Kozunov and Oleg Serikov and Ekaterina Voloshina},
url = {https://megmoscow.ru/wp-content/uploads/pubs/10.1016_j.neuroimage.2024.120868.pdf},
doi = {10.1016/j.neuroimage.2024.120868},
year = {2024},
date = {2024-11-02},
urldate = {2024-11-02},
journal = {NeuroImage},
volume = {301},
pages = {120868},
abstract = {The principle of Representational Similarity Analysis (RSA) posits that neural representations reflect the structure of encoded information, allowing exploration of spatial and temporal organization of brain information processing. Traditional RSA when applied to EEG or MEG data faces challenges in accessing activation time series at the brain source level due to modeling complexities and insufficient geometric/anatomical data. To overcome this, we introduce Representational Dissimilarity Component Analysis (ReDisCA), a method for estimating spatial–temporal components in EEG or MEG responses aligned with a target representational dissimilarity matrix (RDM). ReDisCA yields informative spatial filters and associated topographies, offering insights into the location of ”representationally relevant” sources. Applied to evoked response time series, ReDisCA produces temporal source activation profiles with the desired RDM. Importantly, while ReDisCA does not require inverse modeling its output is consistent with EEG and MEG observation equation and can be used as an input to rigorous source localization procedures. Demonstrating ReDisCA’s efficacy through simulations and comparison with conventional methods, we show superior source localization accuracy and apply the method to real EEG and MEG datasets, revealing physiologically plausible representational structures without inverse modeling. ReDisCA adds to the family of inverse modeling free methods such as independent component analysis (Makeig, 1995), Spatial spectral decomposition (Nikulin, 2011), and Source power comodulation (Dähne, 2014) designed for extraction sources with desired properties from EEG or MEG data. Extending its utility beyond EEG and MEG analysis, ReDisCA is likely to find application in fMRI data analysis and exploration of representational structures emerging in multilayered artificial neural networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The principle of Representational Similarity Analysis (RSA) posits that neural representations reflect the structure of encoded information, allowing exploration of spatial and temporal organization of brain information processing. Traditional RSA when applied to EEG or MEG data faces challenges in accessing activation time series at the brain source level due to modeling complexities and insufficient geometric/anatomical data. To overcome this, we introduce Representational Dissimilarity Component Analysis (ReDisCA), a method for estimating spatial–temporal components in EEG or MEG responses aligned with a target representational dissimilarity matrix (RDM). ReDisCA yields informative spatial filters and associated topographies, offering insights into the location of ”representationally relevant” sources. Applied to evoked response time series, ReDisCA produces temporal source activation profiles with the desired RDM. Importantly, while ReDisCA does not require inverse modeling its output is consistent with EEG and MEG observation equation and can be used as an input to rigorous source localization procedures. Demonstrating ReDisCA’s efficacy through simulations and comparison with conventional methods, we show superior source localization accuracy and apply the method to real EEG and MEG datasets, revealing physiologically plausible representational structures without inverse modeling. ReDisCA adds to the family of inverse modeling free methods such as independent component analysis (Makeig, 1995), Spatial spectral decomposition (Nikulin, 2011), and Source power comodulation (Dähne, 2014) designed for extraction sources with desired properties from EEG or MEG data. Extending its utility beyond EEG and MEG analysis, ReDisCA is likely to find application in fMRI data analysis and exploration of representational structures emerging in multilayered artificial neural networks.
2020
3.
Kozunov, Vladimir V.; West, Timothy O.; Nikolaeva, Anastasia Y.; Stroganova, Tatiana A.; Friston, Karl J. (2020). Object recognition is enabled by an experience-dependent appraisal of visual features in the brain’s value system. NeuroImage, 221, 117143. https://doi.org/10.1016/j.neuroimage.2020.117143
@article{Kozunov2020,
title = {Object recognition is enabled by an experience-dependent appraisal of visual features in the brain’s value system},
author = {Vladimir V. Kozunov and Timothy O. West and Anastasia Y. Nikolaeva and Tatiana A. Stroganova and Karl J. Friston},
url = {https://megmoscow.ru/wp-content/uploads/pubs/10.1016_j.neuroimage.2020.117143.pdf},
doi = {10.1016/j.neuroimage.2020.117143},
issn = {1053-8119},
year = {2020},
date = {2020-11-01},
urldate = {2020-11-01},
journal = {NeuroImage},
volume = {221},
pages = {117143},
publisher = {Elsevier BV},
abstract = {This paper addresses perceptual synthesis by comparing responses evoked by visual stimuli before and after they are recognized, depending on prior exposure. Using magnetoencephalography, we analyzed distributed patterns of neuronal activity – evoked by Mooney figures – before and after they were recognized as meaningful objects. Recognition induced changes were first seen at 100–120 ms, for both faces and tools. These early effects – in right inferior and middle occipital regions – were characterized by an increase in power in the absence of any changes in spatial patterns of activity. Within a later 210–230 ms window, a quite different type of recognition effect appeared. Regions of the brain’s value system (insula, entorhinal cortex and cingulate of the right hemisphere for faces and right orbitofrontal cortex for tools) evinced a reorganization of their neuronal activity without an overall power increase in the region. Finally, we found that during the perception of disambiguated face stimuli, a face-specific response in the right fusiform gyrus emerged at 240–290 ms, with a much greater latency than the well-known N170m component, and, crucially, followed the recognition effect in the value system regions. These results can clarify one of the most intriguing issues of perceptual synthesis, namely, how a limited set of high-level predictions, which is required to reduce the uncertainty when resolving the ill-posed inverse problem of perception, can be available before category-specific processing in visual cortex. We suggest that a subset of local spatial features serves as partial cues for a fast re-activation of object-specific appraisal by the value system. The ensuing top-down feedback from value system to visual cortex, in particular, the fusiform gyrus enables high levels of processing to form category-specific predictions. This descending influence of the value system was more prominent for faces than for tools, the fact that reflects different dependence of these categories on value-related information.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper addresses perceptual synthesis by comparing responses evoked by visual stimuli before and after they are recognized, depending on prior exposure. Using magnetoencephalography, we analyzed distributed patterns of neuronal activity – evoked by Mooney figures – before and after they were recognized as meaningful objects. Recognition induced changes were first seen at 100–120 ms, for both faces and tools. These early effects – in right inferior and middle occipital regions – were characterized by an increase in power in the absence of any changes in spatial patterns of activity. Within a later 210–230 ms window, a quite different type of recognition effect appeared. Regions of the brain’s value system (insula, entorhinal cortex and cingulate of the right hemisphere for faces and right orbitofrontal cortex for tools) evinced a reorganization of their neuronal activity without an overall power increase in the region. Finally, we found that during the perception of disambiguated face stimuli, a face-specific response in the right fusiform gyrus emerged at 240–290 ms, with a much greater latency than the well-known N170m component, and, crucially, followed the recognition effect in the value system regions. These results can clarify one of the most intriguing issues of perceptual synthesis, namely, how a limited set of high-level predictions, which is required to reduce the uncertainty when resolving the ill-posed inverse problem of perception, can be available before category-specific processing in visual cortex. We suggest that a subset of local spatial features serves as partial cues for a fast re-activation of object-specific appraisal by the value system. The ensuing top-down feedback from value system to visual cortex, in particular, the fusiform gyrus enables high levels of processing to form category-specific predictions. This descending influence of the value system was more prominent for faces than for tools, the fact that reflects different dependence of these categories on value-related information.
2018
2.
Kozunov, Vladimir; Nikolaeva, Anastasia; Stroganova, Tatiana A. (2018). Categorization for Faces and Tools—Two Classes of Objects Shaped by Different Experience—Differs in Processing Timing, Brain Areas Involved, and Repetition Effects. Frontiers in Human Neuroscience, 11, 650. https://doi.org/10.3389/fnhum.2017.00650
@article{Kozunov2018,
title = {Categorization for Faces and Tools—Two Classes of Objects Shaped by Different Experience—Differs in Processing Timing, Brain Areas Involved, and Repetition Effects},
author = {Vladimir Kozunov and Anastasia Nikolaeva and Tatiana A. Stroganova},
url = {https://megmoscow.ru/wp-content/uploads/pubs/10.3389_fnhum.2017.00650.pdf},
doi = {10.3389/fnhum.2017.00650},
issn = {1662-5161},
year = {2018},
date = {2018-01-09},
urldate = {2018-01-09},
journal = {Frontiers in Human Neuroscience},
volume = {11},
pages = {650},
publisher = {Frontiers Media SA},
abstract = {The brain mechanisms that integrate the separate features of sensory input into a meaningful percept depend upon the prior experience of interaction with the object and differ between categories of objects. Recent studies using representational similarity analysis (RSA) have characterized either the spatial patterns of brain activity for different categories of objects or described how category structure in neuronal representations emerges in time, but never simultaneously. Here we applied a novel, region-based, multivariate pattern classification approach in combination with RSA to magnetoencephalography data to extract activity associated with qualitatively distinct processing stages of visual perception. We asked participants to name what they see whilst viewing bitonal visual stimuli of two categories predominantly shaped by either value-dependent or sensorimotor experience, namely faces and tools, and meaningless images. We aimed to disambiguate the spatiotemporal patterns of brain activity between the meaningful categories and determine which differences in their processing were attributable to either perceptual categorization per se, or later-stage mentalizing-related processes. We have extracted three stages of cortical activity corresponding to low-level processing, category-specific feature binding, and supra-categorical processing. All face-specific spatiotemporal patterns were associated with bilateral activation of ventral occipito-temporal areas during the feature binding stage at 140–170 ms. The tool-specific activity was found both within the categorization stage and in a later period not thought to be associated with binding processes. The tool-specific binding-related activity was detected within a 210–220 ms window and was located to the intraparietal sulcus of the left hemisphere. Brain activity common for both meaningful categories started at 250 ms and included widely distributed assemblies within parietal, temporal, and prefrontal regions. Furthermore, we hypothesized and tested whether activity within face and tool-specific binding-related patterns would demonstrate oppositely acting effects following procedural perceptual learning. We found that activity in the ventral, face-specific network increased following the stimuli repetition. In contrast, tool processing in the dorsal network adapted by reducing its activity over the repetition period. Altogether, we have demonstrated that activity associated with visual processing of faces and tools during the categorization stage differ in processing timing, brain areas involved, and in their dynamics underlying stimuli learning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The brain mechanisms that integrate the separate features of sensory input into a meaningful percept depend upon the prior experience of interaction with the object and differ between categories of objects. Recent studies using representational similarity analysis (RSA) have characterized either the spatial patterns of brain activity for different categories of objects or described how category structure in neuronal representations emerges in time, but never simultaneously. Here we applied a novel, region-based, multivariate pattern classification approach in combination with RSA to magnetoencephalography data to extract activity associated with qualitatively distinct processing stages of visual perception. We asked participants to name what they see whilst viewing bitonal visual stimuli of two categories predominantly shaped by either value-dependent or sensorimotor experience, namely faces and tools, and meaningless images. We aimed to disambiguate the spatiotemporal patterns of brain activity between the meaningful categories and determine which differences in their processing were attributable to either perceptual categorization per se, or later-stage mentalizing-related processes. We have extracted three stages of cortical activity corresponding to low-level processing, category-specific feature binding, and supra-categorical processing. All face-specific spatiotemporal patterns were associated with bilateral activation of ventral occipito-temporal areas during the feature binding stage at 140–170 ms. The tool-specific activity was found both within the categorization stage and in a later period not thought to be associated with binding processes. The tool-specific binding-related activity was detected within a 210–220 ms window and was located to the intraparietal sulcus of the left hemisphere. Brain activity common for both meaningful categories started at 250 ms and included widely distributed assemblies within parietal, temporal, and prefrontal regions. Furthermore, we hypothesized and tested whether activity within face and tool-specific binding-related patterns would demonstrate oppositely acting effects following procedural perceptual learning. We found that activity in the ventral, face-specific network increased following the stimuli repetition. In contrast, tool processing in the dorsal network adapted by reducing its activity over the repetition period. Altogether, we have demonstrated that activity associated with visual processing of faces and tools during the categorization stage differ in processing timing, brain areas involved, and in their dynamics underlying stimuli learning.
2015
1.
Kozunov, Vladimir V.; Ossadtchi, Alexei (2015). GALA: group analysis leads to accuracy, a novel approach for solving the inverse problem in exploratory analysis of group MEG recordings. Frontiers in Neuroscience, 9, 107. https://doi.org/10.3389/fnins.2015.00107
@article{Kozunov2015,
title = {GALA: group analysis leads to accuracy, a novel approach for solving the inverse problem in exploratory analysis of group MEG recordings},
author = {Vladimir V. Kozunov and Alexei Ossadtchi},
url = {https://megmoscow.ru/wp-content/uploads/pubs/10.3389_fnins.2015.00107.pdf},
doi = {10.3389/fnins.2015.00107},
issn = {1662-453X},
year = {2015},
date = {2015-04-21},
urldate = {2015-04-21},
journal = {Frontiers in Neuroscience},
volume = {9},
pages = {107},
publisher = {Frontiers Media SA},
abstract = {Although MEG/EEG signals are highly variable between subjects, they allow characterizing systematic changes of cortical activity in both space and time. Traditionally a two-step procedure is used. The first step is a transition from sensor to source space by the means of solving an ill-posed inverse problem for each subject individually. The second is mapping of cortical regions consistently active across subjects. In practice the first step often leads to a set of active cortical regions whose location and timecourses display a great amount of interindividual variability hindering the subsequent group analysis. We propose Group Analysis Leads to Accuracy (GALA)—a solution that combines the two steps into one. GALA takes advantage of individual variations of cortical geometry and sensor locations. It exploits the ensuing variability in electromagnetic forward model as a source of additional information. We assume that for different subjects functionally identical cortical regions are located in close proximity and partially overlap and their timecourses are correlated. This relaxed similarity constraint on the inverse solution can be expressed within a probabilistic framework, allowing for an iterative algorithm solving the inverse problem jointly for all subjects. A systematic simulation study showed that GALA, as compared with the standard min-norm approach, improves accuracy of true activity recovery, when accuracy is assessed both in terms of spatial proximity of the estimated and true activations and correct specification of spatial extent of the activated regions. This improvement obtained without using any noise normalization techniques for both solutions, preserved for a wide range of between-subject variations in both spatial and temporal features of regional activation. The corresponding activation timecourses exhibit significantly higher similarity across subjects. Similar results were obtained for a real MEG dataset of face-specific evoked responses.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Although MEG/EEG signals are highly variable between subjects, they allow characterizing systematic changes of cortical activity in both space and time. Traditionally a two-step procedure is used. The first step is a transition from sensor to source space by the means of solving an ill-posed inverse problem for each subject individually. The second is mapping of cortical regions consistently active across subjects. In practice the first step often leads to a set of active cortical regions whose location and timecourses display a great amount of interindividual variability hindering the subsequent group analysis. We propose Group Analysis Leads to Accuracy (GALA)—a solution that combines the two steps into one. GALA takes advantage of individual variations of cortical geometry and sensor locations. It exploits the ensuing variability in electromagnetic forward model as a source of additional information. We assume that for different subjects functionally identical cortical regions are located in close proximity and partially overlap and their timecourses are correlated. This relaxed similarity constraint on the inverse solution can be expressed within a probabilistic framework, allowing for an iterative algorithm solving the inverse problem jointly for all subjects. A systematic simulation study showed that GALA, as compared with the standard min-norm approach, improves accuracy of true activity recovery, when accuracy is assessed both in terms of spatial proximity of the estimated and true activations and correct specification of spatial extent of the activated regions. This improvement obtained without using any noise normalization techniques for both solutions, preserved for a wide range of between-subject variations in both spatial and temporal features of regional activation. The corresponding activation timecourses exhibit significantly higher similarity across subjects. Similar results were obtained for a real MEG dataset of face-specific evoked responses.