scholarly journals Moving Beyond the Mean: Subgroups and dimensions of brain activity and cognitive performance across domains

2020 ◽  
Author(s):  
Colin Hawco ◽  
Erin W. Dickie ◽  
Grace Jacobs ◽  
Zafiris J. Daskalakis ◽  
Aristotle N. Voineskos
Keyword(s):  
Brain Activity ◽  
Individual Variability ◽  
Cognitive Test ◽  
Cognitive Tasks ◽  
Similarity Space ◽  
Human Connectome Project ◽  
The Mean ◽  
The Rich ◽  
Cluster Probability ◽  
Human Neuroimaging

AbstractHuman neuroimaging during cognitive tasks has provided unique and important insights into the neurobiology of cognition. However, the vast majority of research relies upon group aggregate or average statistical maps of activity, which do not fully capture the rich variability which exists across individuals. To better characterize individual variability, hierarchical clustering was performed separately on six fMRI tasks in 822 participants from the Human Connectome Project. Across all tasks, clusters ranged from a predominantly ‘deactivating’ pattern towards a more ‘activating’ pattern of brain activity, with differences in out-of-scanner cognitive test scores between clusters. Cluster stability was assessed via bootstrapping approach. Cluster probability did not indicate distinct/clear clustering. However, when participants were plotted in a dimensionally reduced ‘similarity space’ derived from bootstrapping, variability in brain activity among participants was best represented multidimensionally. A ‘positive to negative’ axis of activity was the strongest driver of individual differences.

scholarly journals DREAM

2021 ◽  
Author(s):  
Zhu-Qing Gong ◽  
Peng Gao ◽  
Chao Jiang ◽  
Xiu-Xia Xing ◽  
Hao-Ming Dong ◽  
...  
Keyword(s):  
Brain Activity ◽  
Worked Examples ◽  
Individual Variability ◽  
Head Movements ◽  
Neural Oscillations ◽  
Head Motion ◽  
Multiple Frequency ◽  
Frequency Bands ◽  
Human Connectome Project ◽  
The Brain

AbstractRhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM decoded the head motion oscillations and uncovered that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age of 7 to 9 years. It is interesting that the higher frequency bands contain more head movements, and showed stronger age-motion associations but weaker sex-motion interactions. Using data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. The resting-state brain ranks its spontaneous oscillation’s amplitudes spatially from high in ventral-temporal areas to low in ventral-occipital areas when the frequency band increased from low to high, while those in part of parietal and ventral frontal regions are reversed. The higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, DREAM adds a reliable and valid tool to mapping human brain function from a multiple-frequency window into brain waves.

scholarly journals Multi-dynamic Modelling Reveals Strongly Time-varying Resting fMRI Correlations

2021 ◽  
Author(s):  
Usama Pervaiz ◽  
Diego Vidaurre ◽  
Chetan Gohil ◽  
Stephen M. Smith ◽  
Mark W Woolrich
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Dynamic Modelling ◽  
Fmri Data ◽  
Generative Adversarial Networks ◽  
Activity Levels ◽  
Time Varying ◽  
Human Connectome Project ◽  
The Mean ◽  
Over Time

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time-varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability

scholarly journals Proximity of the middle meningeal artery and maxillary artery to the mandibular head and mandibular neck as revealed by three-dimensional time-of-flight magnetic resonance angiography

2021 ◽  
Author(s):  
Daphne Schönegg ◽  
Raphael Ferrari ◽  
Julian Ebner ◽  
Michael Blumer ◽  
Martin Lanzer ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Angiography ◽  
Three Dimensional ◽  
Time Of Flight ◽  
Middle Meningeal Artery ◽  
Individual Variability ◽  
Maxillary Artery ◽  
Condylar Process ◽  
The Mean ◽  
Meningeal Artery

Abstract Purpose The close topographic relationship between vascular and osseous structures in the condylar and subcondylar region and marked variability in the arterial course has been revealed by both imaging and cadaveric studies. This study aimed to verify the previously published information in a large sample and to determine a safe surgical region. Methods We analyzed the three-dimensional time-of-flight magnetic resonance angiography images of 300 individuals. Results The mean distance between the middle meningeal artery and the apex of the condyle or the most medial point of the condyle was 18.8 mm (range: 11.2–25.9 mm) or 14.5 mm (range: 8.8–22.9 mm) respectively. The course of the maxillary artery relative to the lateral pterygoid muscle was medial in 45.7% of cases and lateral in 54.3%. An asymmetric course was evident in 66 patients (22%). The mean distance between the maxillary artery and condylar process at the deepest point of the mandibular notch was 6.2 mm in sides exhibiting a medial course (range: 3.7–9.8 mm) and 6.6 mm in sides exhibiting a lateral course (range: 3.9–10.4 mm). The distances were significantly influenced by age, gender, and the course of the maxillary artery. Conclusion Our study emphasizes the marked inter- and intra-individual variability of the maxillary and middle meningeal arterial courses. We confirmed the proximity of the arteries to the condylar process. Extensive surgical experience and thorough preparation for each individual case are essential to prevent iatrogenic vascular injury.

scholarly journals Rates of Cognitive and Functional Impairments in Older Adults Residing in a Continuing Care Senior Housing Community

2021 ◽  
pp. 1-12
Author(s):  
Ryan Van Patten ◽  
Zanjbeel Mahmood ◽  
Tanya T. Nguyen ◽  
Jacqueline E. Maye ◽  
Ho-Cheol Kim ◽  
...  
Keyword(s):  
Older Adults ◽  
Functional Capacity ◽  
Independent Living ◽  
Visual Learning ◽  
Cognitive Test ◽  
Continuing Care ◽  
Cognitive Screening ◽  
Functional Impairments ◽  
Senior Housing ◽  
The Mean

Abstract Objective: The current cross-sectional study examined cognition and performance-based functional abilities in a continuing care senior housing community (CCSHC) that is comparable to other CCSHCs in the US with respect to residents’ demographic characteristics. Method: Participants were 110 older adult residents of the independent living unit. We assessed sociodemographics, mental health, neurocognitive functioning, and functional capacity. Results: Compared to normative samples, participants performed at or above expectations in terms of premorbid functioning, attention span and working memory, processing speed, timed set-shifting, inhibitory control, and confrontation naming. They performed below expectation in verbal fluency and verbal and visual learning and memory, with impairment rates [31.4% (>1 SD below the mean) and 18.49% (>1.5 SD below the mean)] well above the general population (16% and 7%, respectively). Within the cognitive test battery, two tests of delayed memory were most predictive of a global deficit score. Most cognitive test scores correlated with performance-based functional capacity. Conclusions: Overall, results suggest that a subset of older adults in the independent living sector of CCSHCs are cognitively and functionally impaired and are at risk for future dementia. Results also argue for the inclusion of memory tests in abbreviated screening batteries in this population. We suggest that CCSHCs implement regular cognitive screening procedures to identify and triage those older adults who could benefit from interventions and, potentially, a transition to a higher level of care.

scholarly journals Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Resting State ◽  
Early Life ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Noxious Stimulus ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

scholarly journals Association Between Brain Activation and Functional Connectivity

2018 ◽  
Vol 29 (5) ◽  
pp. 1984-1996 ◽  
Author(s):  
Dardo Tomasi ◽  
Nora D Volkow
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Low Frequency ◽  
Brain Activation ◽  
Blood Oxygen Level Dependent ◽  
Common Source ◽  
Functional Connectivity Density ◽  
Density Mapping ◽  
Human Connectome Project ◽  
Bold Responses

Abstract The origin of the “resting-state” brain activity recorded with functional magnetic resonance imaging (fMRI) is still uncertain. Here we provide evidence for the neurovascular origins of the amplitude of the low-frequency fluctuations (ALFF) and the local functional connectivity density (lFCD) by comparing them with task-induced blood-oxygen level dependent (BOLD) responses, which are considered a proxy for neuronal activation. Using fMRI data for 2 different tasks (Relational and Social) collected by the Human Connectome Project in 426 healthy adults, we show that ALFF and lFCD have linear associations with the BOLD response. This association was significantly attenuated by a novel task signal regression (TSR) procedure, indicating that task performance enhances lFCD and ALFF in activated regions. We also show that lFCD predicts BOLD activation patterns, as was recently shown for other functional connectivity metrics, which corroborates that resting functional connectivity architecture impacts brain activation responses. Thus, our findings indicate a common source for BOLD responses, ALFF and lFCD, which is consistent with the neurovascular origin of local hemodynamic synchrony presumably reflecting coordinated fluctuations in neuronal activity. This study also supports the development of task-evoked functional connectivity density mapping.

scholarly journals Impact of Visual Design Elements and Principles in Human Electroencephalogram Brain Activity Assessed with Spectral Methods and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4695
Author(s):  
Francisco E. Cabrera ◽  
Pablo Sánchez-Núñez ◽  
Gustavo Vaccaro ◽  
José Ignacio Peláez ◽  
Javier Escudero
Keyword(s):  
Brain Activity ◽  
Music Videos ◽  
Visual Design ◽  
Music Video ◽  
Eeg Signal ◽  
Design Elements ◽  
The Public ◽  
Learning Classifier ◽  
The Mean ◽  
Human Electroencephalogram

The visual design elements and principles (VDEPs) can trigger behavioural changes and emotions in the viewer, but their effects on brain activity are not clearly understood. In this paper, we explore the relationships between brain activity and colour (cold/warm), light (dark/bright), movement (fast/slow), and balance (symmetrical/asymmetrical) VDEPs. We used the public DEAP dataset with the electroencephalogram signals of 32 participants recorded while watching music videos. The characteristic VDEPs for each second of the videos were manually tagged for by a team of two visual communication experts. Results show that variations in the light/value, rhythm/movement, and balance in the music video sequences produce a statistically significant effect over the mean absolute power of the Delta, Theta, Alpha, Beta, and Gamma EEG bands (p < 0.05). Furthermore, we trained a Convolutional Neural Network that successfully predicts the VDEP of a video fragment solely by the EEG signal of the viewer with an accuracy ranging from 0.7447 for Colour VDEP to 0.9685 for Movement VDEP. Our work shows evidence that VDEPs affect brain activity in a variety of distinguishable ways and that a deep learning classifier can infer visual VDEP properties of the videos from EEG activity.

Studying the effect of Heartfulness Meditation on Brain Activity

2018 ◽  
Author(s):  
Pallavi Gupta ◽  
Jahnavi Mundluru ◽  
Arth Patel ◽  
Shankar Pathmakanthan
Keyword(s):  
Problem Solving ◽  
Creative Thinking ◽  
Brain Activity ◽  
Cognitive Task ◽  
Thinking Skills ◽  
Cognitive Tasks ◽  
Logical Thinking ◽  
Meditation Practice ◽  
Guided Relaxation

Long-term meditation practice is increasingly recognized for its health benefits. Heartfulness meditation represents a quickly growing set of practices that is largely unstudied. Heartfulness is unique in that it is a meditation practice that focuses on the Heart. It helps individuals to connect to themselves and find inner peace. In order to deepen ones’ meditation, the element of Yogic Energy (‘pranahuti’) is used as an aid during meditation. The purpose of this study was to determine whether consistent EEG effects of Heartfulness meditation be observed in sixty experienced Heartfulness meditators, each of whom attended 6 testing sessions. In each session, participants performed three conditions: a set of cognitive tasks, Heartfulness guided relaxation, and Heartfulness Meditation. Participants during the cognitive portion were required to answer questions that tested their logical thinking (Cognitive Reflective Test) and creative thinking skills. (Random Associative Test) The order of condition was randomly counter balanced across six sessions. It was hypothesized that Heartfulness meditation would bring increased alpha (8-12Hz) brain activity during meditation and better cognitive task scores in sessions where the tasks followed meditation. Heartfulness meditation produces a significant decrease in brain activity (as indexed by higher levels of alpha during the early stages of meditation. As the meditation progressed deep meditative state (as indexed by higher levels of delta) were observed until the end of the condition.  This lead to the conclusion that Heartfulness Meditation produces a state that is clearly distinguishable from effortful problem solving. 

scholarly journals Leveling up: improving power in fMRI by moving beyond cluster-level inference

2021 ◽  
Author(s):  
Stephanie Noble ◽  
Mandy Mejia ◽  
Andrew Zalesky ◽  
Dustin Scheinost
Keyword(s):  
Average Effect ◽  
Brain Areas ◽  
Whole Brain ◽  
Typical Sample ◽  
Human Connectome Project ◽  
Inference Network ◽  
The Brain ◽  
Cluster Level ◽  
Human Neuroimaging ◽  
Adequate Power

Inference in neuroimaging commonly occurs at the level of "clusters" of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the "tip of the iceberg" of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed "adequate" power (β =80%) to detect an average effect, with almost double the power for network- compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.

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