scholarly journals Focal neural perturbations reshape low-dimensional trajectories of brain activity supporting cognitive performance

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Kartik K. Iyer ◽  
Kai Hwang ◽  
Luke J. Hearne ◽  
Eli Muller ◽  
Mark D’Esposito ◽  
...  
Keyword(s):  
Working Memory ◽  
Neural Activity ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Memory Performance ◽  
Functional Link ◽  
Low Dimensional Topology ◽  
Brain Functions ◽  
Human Participants ◽  
Low Dimensional

AbstractThe emergence of distributed patterns of neural activity supporting brain functions and behavior can be understood by study of the brain’s low-dimensional topology. Functional neuroimaging demonstrates that brain activity linked to adaptive behavior is constrained to low-dimensional manifolds. In human participants, we tested whether these low-dimensional constraints preserve working memory performance following local neuronal perturbations. We combined multi-session functional magnetic resonance imaging, non-invasive transcranial magnetic stimulation (TMS), and methods translated from the fields of complex systems and computational biology to assess the functional link between changes in local neural activity and the reshaping of task-related low dimensional trajectories of brain activity. We show that specific reconfigurations of low-dimensional trajectories of brain activity sustain effective working memory performance following TMS manipulation of local activity on, but not off, the space traversed by these trajectories. We highlight an association between the multi-scale changes in brain activity underpinning cognitive function.

scholarly journals Drifting codes within a stable coding scheme for working memory

2019 ◽  
Author(s):  
M. J. Wolff ◽  
J. Jochim ◽  
E. G. Akyürek ◽  
T. J. Buschman ◽  
M. G. Stokes
Keyword(s):  
Working Memory ◽  
Brain Activity ◽  
Memory Performance ◽  
Specific Impulse ◽  
Mental States ◽  
Dynamic Component ◽  
Coding Scheme ◽  
Stable Maintenance ◽  
Low Dimensional ◽  
Time Specific

AbstractWorking memory (WM) is important to maintain information over short time periods to provide some stability in a constantly changing environment. However, brain activity is inherently dynamic, raising a challenge for maintaining stable mental states. To investigate the relationship between WM stability and neural dynamics, we used electroencephalography to measure the neural response to impulse stimuli during a WM delay. Multivariate pattern analysis revealed representations were both stable and dynamic: there was a clear difference in neural states between time-specific impulse responses, reflecting dynamic changes, yet the coding scheme for memorized orientations was stable. This suggests that a stable subcomponent in WM enables stable maintenance within a dynamic system. A stable coding scheme simplifies readout for WM-guided behaviour, whereas the low-dimensional dynamic component could provide additional temporal information. Despite having a stable subspace, WM is clearly not perfect – memory performance still degrades over time. Indeed, we find that even within the stable coding scheme, memories drift during maintenance. When averaged across trials, such drift contributes to the width of the error distribution.

scholarly journals Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance

2021 ◽  
Author(s):  
Adeline Jabès ◽  
Giuliana Klencklen ◽  
Paolo Ruggeri ◽  
Christoph M. Michel ◽  
Pamela Banta Lavenex ◽  
...  
Keyword(s):  
Older Adults ◽  
Working Memory ◽  
Resting State ◽  
Cognitive Aging ◽  
Temporal Dynamics ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Age Related

AbstractAlterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

scholarly journals Urinary tetrahydrocannabinol is associated with poorer working memory performance and alterations in associated brain activity

2018 ◽  
Vol 44 (3) ◽  
pp. 613-619 ◽  
Author(s):  
Max M. Owens ◽  
Shannon McNally ◽  
Tashia Petker ◽  
Michael T. Amlung ◽  
Iris M. Balodis ◽  
...  
Keyword(s):  
Working Memory ◽  
Brain Activity ◽  
Memory Performance

scholarly journals Age-Related Differences in Resting-State EEG and Allocentric Spatial Working Memory Performance

2021 ◽  
Vol 13 ◽  
Author(s):  
Adeline Jabès ◽  
Giuliana Klencklen ◽  
Paolo Ruggeri ◽  
Jean-Philippe Antonietti ◽  
Pamela Banta Lavenex ◽  
...  
Keyword(s):  
Older Adults ◽  
Working Memory ◽  
Resting State ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Memory Performance ◽  
Group Differences ◽  
Age Group ◽  
Eeg Activity ◽  
Recording Conditions

During normal aging resting-state brain activity changes and working memory performance declines as compared to young adulthood. Interestingly, previous studies reported that different electroencephalographic (EEG) measures of resting-state brain activity may correlate with working memory performance at different ages. Here, we recorded resting-state EEG activity and tested allocentric spatial working memory in healthy young (20–30 years) and older (65–75 years) adults. We adapted standard EEG methods to record brain activity in mobile participants in a non-shielded environment, in both eyes closed and eyes open conditions. Our study revealed some age-group differences in resting-state brain activity that were consistent with previous results obtained in different recording conditions. We confirmed that age-group differences in resting-state EEG activity depend on the recording conditions and the specific parameters considered. Nevertheless, lower theta-band and alpha-band frequencies and absolute powers, and higher beta-band and gamma-band relative powers were overall observed in healthy older adults, as compared to healthy young adults. In addition, using principal component and regression analyses, we found that the first extracted EEG component, which represented mainly theta, alpha and beta powers, correlated with spatial working memory performance in older adults, but not in young adults. These findings are consistent with the theory that the neurobiological bases of working memory performance may differ between young and older adults. However, individual measures of resting-state EEG activity could not be used as reliable biomarkers to predict individual allocentric spatial working memory performance in young or older adults.

scholarly journals The Dynamic-Processing Model of Working Memory

2020 ◽  
Vol 29 (4) ◽  
pp. 378-387
Author(s):  
Nathan S. Rose
Keyword(s):  
Working Memory ◽  
Brain Activity ◽  
Memory Performance ◽  
Short Term ◽  
Dynamic Activity ◽  
Mind And Brain ◽  
Latent Representations ◽  
Dynamic Processing ◽  
Conventional Models ◽  
The Mind

Recent shifts in the understanding of how the mind and brain retain information in working memory (WM) call for revision to traditional theories. Evidence of dynamic, “activity-silent,” short-term retention processes diverges from conventional models positing that information is always retained in WM by sustained neural activity in buffers. Such evidence comes from machine-learning methods that can decode patterns of brain activity and the simultaneous administration of transcranial magnetic stimulation (TMS) to causally manipulate brain activity in specific areas and time points. TMS can “ping” brain areas to both reactivate latent representations retained in WM and affect memory performance. On the basis of these findings, I argue for a supplement to sustained retention mechanisms. Brain-decoding methods also reveal that dynamic levels of representational codes are retained in WM, and these vary according to task context, from perceptual (sensory) codes in posterior areas to abstract, recoded representations distributed across frontoparietal regions. A dynamic-processing model of WM is advanced to account for the overall pattern of results.

Imagine imaging neural activity in crying infants and in their caring parents

2004 ◽  
Vol 27 (4) ◽  
pp. 465-467 ◽  
Author(s):  
Steven Laureys ◽  
Serge Goldman
Keyword(s):  
Neural Activity ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Objective Measurement ◽  
Signal Function ◽  
Infant Crying ◽  
Early Infant ◽  
Neural Substrate

Soltis' paper contains little data on the underlying neural substrate of the discussed signal function of early infant crying – probably because there is amazingly little known about it. We here discuss the interest of functional neuroimaging as an objective measurement of brain activity in (1) early infants during crying and (2) parents hearing their offspring cry.

Why the Assessment of Causality in Brain–Behavior Relations Requires Brain Stimulation

2012 ◽  
Vol 24 (4) ◽  
pp. 775-777 ◽  
Author(s):  
Juha Silvanto ◽  
Alvaro Pascual-Leone
Keyword(s):  
Opposite Direction ◽  
Brain Stimulation ◽  
Cognitive Neuroscience ◽  
Neural Activity ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Causal Link ◽  
And Behavior ◽  
Brain Behavior

A central aim in cognitive neuroscience is to explain how neural activity gives rise to perception and behavior; the causal link of paramount interest is thus from brain to behavior. Functional neuroimaging studies, however, tend to provide information in the opposite direction by informing us how manipulation of behavior may affect neural activity. Although this may provide valuable insights into neuronal properties, one cannot use such evidence to make inferences about the behavioral significance of the observed activations; if A causes B, it does not necessarily follow that B causes A. In contrast, brain stimulation techniques enable us to directly modulate brain activity as the source of behavior and thus establish causal links.

scholarly journals Brain activity is contingent on neuropsychological function in an fMRI study of Verbal Working Memory in Amyotrophic Lateral Sclerosis

2021 ◽  
Author(s):  
Xenia Kobeleva ◽  
Judith Machts ◽  
Maria Veit ◽  
Stefan Vielhaber ◽  
Susanne Petri ◽  
...  
Keyword(s):  
Working Memory ◽  
Amyotrophic Lateral Sclerosis ◽  
Functional Neuroimaging ◽  
Current Knowledge ◽  
Brain Activity ◽  
Reaction Times ◽  
Verbal Working Memory ◽  
Research Field ◽  
Fmri Study ◽  
Lateral Sclerosis

AbstractAmyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that causes progressive degeneration of neurons in motor and non-motor regions, affecting multiple cognitive domains. In order to contribute to the growing research field that employs structural and functional neuroimaging to investigate the effect of ALS on different working memory components, we conducted a functional magnetic resonance imaging (fMRI) study exploring the localization and intensity of alterations in neural activity. Being the first study to specifically address verbal working memory via fMRI in the context of ALS, we employed the verbal n-back task with 0-back and 2-back conditions. Despite ALS patients showing unimpaired accuracies and reaction times, there was significantly increased brain activity of frontotemporal and parietal regions in the 2-back minus 0-back contrast in patients compared to controls. This increased brain activity was largely associated with a better neuropsychological performance within the ALS group, suggesting a compensatory effect. This study therefore adds to the current knowledge on neural correlates of working memory in ALS and contributes to a more nuanced understanding of hyperactivity during cognitive processes in fMRI studies of ALS.

scholarly journals High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lucy L. W. Owen ◽  
Thomas H. Chang ◽  
Jeremy R. Manning
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Dynamic Network ◽  
Brain Structures ◽  
High Order ◽  
Dynamic Correlations ◽  
Neuroimaging Data ◽  
Human Participants ◽  
High Level

AbstractOur thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain’s functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

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