Integrative Visuomotor Behavior Is Associated With Interregionally Coherent Oscillations in the Human Brain

1998 ◽  
Vol 79 (3) ◽  
pp. 1567-1573 ◽  
Author(s):  
Joseph Classen ◽  
Christian Gerloff ◽  
Manabu Honda ◽  
Mark Hallett
Keyword(s):  
Motor Cortex ◽  
Human Brain ◽  
Large Scale ◽  
Isometric Force ◽  
Brain Activity ◽  
Motor Task ◽  
Low Frequency ◽  
Visuomotor Behavior ◽  
Electrical Brain Activity ◽  
Eeg Recordings

Classen, Joseph, Christian Gerloff, Manabu Honda, and Mark Hallett. Integrative visuomotor behavior is associated with interregionally coherent oscillations in the human brain. J. Neurophysiol. 79: 1567–1573, 1998. Coherent electrical brain activity has been demonstrated to be associated with perceptual events in mammals. It is unclear whether or not it is also a mechanism instrumental in the performance of sensorimotor tasks requiring the continuous processing of information between primarily executive and receptive brain areas. In particular it is unknown whether or not interregional coherent activity detectable in electroencephalographic (EEG) recordings on the scalp reflects interareal functional cooperativity in humans. We studied patterns of changes in EEG-coherence associated with a visuomotor force-tracking task in seven subjects. Interregional coherence of EEG signals recorded from scalp regions overlying the visual and the motor cortex increased in comparison to a resting condition when subjects tracked a visual target by producing an isometric force with their right index finger. Coherence between visual and motor cortex decreased when the subjects produced a similar motor output in the presence of a visual distractor and was unchanged in a purely visual and purely motor task. Increases and decreases of coherence were best differentiated in the low beta frequency range (13–21 Hz). This observation suggests a special functional significance of low frequency oscillations in information processing in large-scale networks. These findings substantiate the view that coherent brain activity underlies integrative sensorimotor behavior.

Is Artistic Composition in Abstract Art Detected Automatically?

2021 ◽  
pp. 102-106
Author(s):  
Claudia Menzel ◽  
Gyula Kovács ◽  
Gregor U. Hayn-Leichsenring ◽  
Christoph Redies
Keyword(s):  
Human Brain ◽  
Significant Role ◽  
Mismatch Negativity ◽  
Brain Activity ◽  
Perceptual Processing ◽  
Abstract Art ◽  
Electrical Brain Activity ◽  
Image Set ◽  
The Brain

Most artists who create abstract paintings place the pictorial elements not at random, but arrange them intentionally in a specific artistic composition. This arrangement results in a pattern of image properties that differs from image versions in which the same pictorial elements are randomly shuffled. In the article under discussion, the original abstract paintings of the author’s image set were rated as more ordered and harmonious but less interesting than their shuffled counterparts. The authors tested whether the human brain distinguishes between these original and shuffled images by recording electrical brain activity in a particular paradigm that evokes a so-called visual mismatch negativity. The results revealed that the brain detects the differences between the two types of images fast and automatically. These findings are in line with models that postulate a significant role of early (low-level) perceptual processing of formal image properties in aesthetic evaluations.

scholarly journals Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes

2020 ◽  
Author(s):  
Felix Bröhl ◽  
Christoph Kayser
Keyword(s):  
High Frequency ◽  
Brain Activity ◽  
Low Frequency ◽  
List Type ◽  
Theta Band ◽  
Acoustic Fluctuations ◽  
Eeg Recordings ◽  
Band Limited ◽  
Speech Envelope ◽  
Speech Tracking

AbstractThe representation of speech in the brain is often examined by measuring the alignment of rhythmic brain activity to the speech envelope. To conveniently quantify this alignment (termed ‘speech tracking’) many studies consider the overall speech envelope, which combines acoustic fluctuations across the spectral range. Using EEG recordings, we show that using this overall envelope can provide a distorted picture on speech encoding. We systematically investigated the encoding of spectrally-limited speech-derived envelopes presented by individual and multiple noise carriers in the human brain. Tracking in the 1 to 6 Hz EEG bands differentially reflected low (0.2 – 0.83 kHz) and high (2.66 – 8 kHz) frequency speech-derived envelopes. This was independent of the specific carrier frequency but sensitive to attentional manipulations, and reflects the context-dependent emphasis of information from distinct spectral ranges of the speech envelope in low frequency brain activity. As low and high frequency speech envelopes relate to distinct phonemic features, our results suggest that functionally distinct processes contribute to speech tracking in the same EEG bands, and are easily confounded when considering the overall speech envelope.HighlightsDelta/theta band EEG tracks band-limited speech-derived envelopes similar to real speechLow and high frequency speech-derived envelopes are represented differentiallyHigh-frequency derived envelopes are more susceptible to attentional and contextual manipulationsDelta band tracking shifts towards low frequency derived envelopes with more acoustic detail

scholarly journals Post-stroke fatigue is linked to resting state posterior hypoactivity and prefrontal hyperactivity

2021 ◽  
Author(s):  
Georgia Mary Cotter ◽  
Mohamed Salah Khlif ◽  
Laura Bird ◽  
Mark E Howard ◽  
Amy Brodtmann ◽  
...  
Keyword(s):  
Ischaemic Stroke ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Stroke Severity ◽  
Stroke Survivors ◽  
Whole Brain ◽  
Post Stroke

Background and Purpose. Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Methods. This observational study included 63 stroke survivors (22 women; age 30-89 years; mean 67.5 years) from the Cognition And Neocortical Volume After Stroke (CANVAS) study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischaemic stroke. Participants underwent brain imaging 3 months post-stroke, including a 7-minute resting state fMRI echoplanar sequence. We calculated the fractional amplitude of low-frequency fluctuations, a measure of resting state brain activity at the whole-brain level. Results. Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. A generalised linear regression model analysis with age, sex, and stroke severity covariates was conducted to compare resting state brain activity in the 0.01-0.08 Hz range, as well as its subcomponents - slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz) frequency bands between fatigued and non-fatigued participants. We found no significant associations between post-stroke fatigue and ischaemic stroke lesion location or stroke volume. However, in the overall 0.01-0.08 Hz band, participants with post-stroke fatigue demonstrated significantly lower resting-state activity in the calcarine cortex (p<0.001, cluster-corrected pFDR=0.009, k=63) and lingual gyrus (p<0.001, cluster-corrected pFDR=0.025, k=42) and significantly higher activity in the medial prefrontal cortex (p<0.001, cluster-corrected pFDR=0.03, k=45), attributed to slow-4 and slow-5 oscillations, respectively. Conclusions. Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity, reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This first whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.

scholarly journals How the human brain introspects about one’s own episodes of cognitive control

2017 ◽  
Author(s):  
David Soto ◽  
Mona Theodoraki ◽  
Pedro M. Paz-Alonso
Keyword(s):  
Cognitive Control ◽  
Human Brain ◽  
Internal Model ◽  
Large Scale ◽  
Brain Activity ◽  
Temporal Correlation ◽  
Control Networks ◽  
Control Functions ◽  
Cognitive States ◽  
Behavioural Performance

AbstractMetacognition refers to our capacity to reflect upon our experiences, thoughts and actions. Metacognition processes are linked to cognitive control functions that allow keeping our actions on-task. But it is unclear how the human brain builds an internal model of one’s cognition and behaviour. We conducted 2 fMRI experiments in which brain activity was recorded ‘online’ as participants engaged in a memory-guided search task and then later ‘offline’ when participants introspected about their prior experience and cognitive states during performance. In Experiment 1 the memory cues were task-relevant while in Experiment 2 they were irrelevant. Across Experiments, the patterns of brain activity, including frontoparietal regions, were similar during on-task and introspection states. However the connectivity profile amongst frontoparietal areas was distint during introspection and modulated by the relevance of the memory cues. Introspection was also characterized by increased temporal correlation between the default-mode network (DMN), frontoparietal and dorsal attention networks and visual cortex. We suggest that memories of one’s own experience during task performance are encoded in large-scale patterns of brain activity and that coupling between DMN and frontoparietal control networks may be crucial to build an internal model of one’s behavioural performance.

scholarly journals Application and Evolution for Neural Network and Signal Processing in Large-Scale Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dongbao Jia ◽  
Cunhua Li ◽  
Qun Liu ◽  
Qin Yu ◽  
Xiangsheng Meng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Large Scale ◽  
Brain Activity ◽  
Low Frequency ◽  
Frequency Characteristics ◽  
Brain Diseases ◽  
Time Dimension ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Low Frequency Power

Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation (ALFF) is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform (FSST), which is often used in the field of signal processing to extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform (FFT) through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2019 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2020 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

Post-stroke fatigue is associated with resting state posterior hypoactivity and prefrontal hyperactivity

2021 ◽  
pp. 174749302110483
Author(s):  
Georgia Cotter ◽  
Mohamed Salah Khlif ◽  
Laura Bird ◽  
Mark E Howard ◽  
Amy Brodtmann ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Low Frequency ◽  
Stroke Survivors ◽  
Whole Brain ◽  
Post Stroke ◽  
State Study ◽  
Stroke Study ◽  
Patient Health

Background Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Aim This observational study aimed to identify resting state brain activity markers of post-stroke fatigue. Method Sixty-three stroke survivors (22 women; age 30–89 years; mean 67.5 ± 13.4 years) from the Cognition And Neocortical Volume After Stroke study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischemic stroke, underwent brain imaging three months post-stroke, including a 7-minute resting state functional magnetic resonance imaging. We calculated the fractional amplitude of low-frequency fluctuations, which is measured at the whole-brain level and can detect altered spontaneous neural activity of specific regions. Results Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. Fatigued compared to non-fatigued participants demonstrated significantly lower resting-state activity in the calcarine cortex ( p < 0.001, cluster-corrected pFDR = 0.009, k = 63) and lingual gyrus ( p < 0.001, cluster-corrected pFDR = 0.025, k = 42) and significantly higher activity in the medial prefrontal cortex ( p < 0.001, cluster-corrected pFDR = 0.03, k = 45). Conclusions Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.

scholarly journals Monolingual and bilingual infants rely on the same brain networks: Evidence from resting-state functional connectivity

2020 ◽  
Author(s):  
Borja Blanco ◽  
Monika Molnar ◽  
Manuel Carreiras ◽  
Liam H. Collins-Jones ◽  
Ernesto Vidal ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Near Infrared ◽  
Functional Organization ◽  
Brain Activity ◽  
Functional Brain Imaging ◽  
Resting State Functional Connectivity ◽  
Functional Near Infrared Spectroscopy ◽  
Behavioural Research ◽  
Infant Brain

AbstractThis study examines whether bilingual exposure has a profound effect on the functional organization of the developing human brain during infancy. Recent behavioural research attests that monolingual vs. bilingual experience affects cognitive and linguistic processes already during the first months of life. However, to what extent the intrinsic organization of the infant human brain adapts to monolingual vs. bilingual environments is unclear. We measured spontaneous hemodynamic brain activity using functional near-infrared spectroscopy (fNIRS) in a large cohort (N=99) of 4-month-old monolingual and bilingual infants. We implemented well-established analysis approaches of functional brain imaging that enabled us to reveal the functional organization of the infant brain in large-scale cortical networks, and to perform group-level comparisons (i.e., monolingual vs. bilingual groups) in a reliable manner. Our results revealed no differences between the intrinsic functional organization of the developing monolingual and bilingual infant brain at 4 months of age.

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

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