scholarly journals Towards a “Treadmill Test” for Cognition: Reliable Prediction of Intelligence From Whole-Brain Task Activation Patterns

2018 ◽  
Author(s):  
Chandra Sripada ◽  
Mike Angstadt ◽  
Saige Rutherford
Keyword(s):  
Gray Matter Volume ◽  
Memory Task ◽  
Whole Brain ◽  
Activation Patterns ◽  
Executive Processing ◽  
Human Connectome Project ◽  
Treadmill Testing ◽  
Intelligence Scores ◽  
The Brain ◽  
Task Activation

AbstractIdentifying brain-based markers of general cognitive ability, i.e., “intelligence”, has been a longstanding goal of cognitive and clinical neuroscience. Previous studies focused on relatively static, enduring features such as gray matter volume and white matter structure. In this report, we investigate prediction of intelligence based on task activation patterns during theN-back working memory task as well as six other tasks in the Human Connectome Project dataset, encompassing 19 task contrasts. We find that whole brain task activation patterns are a highly effective basis for prediction of intelligence, achieving a 0.68 correlation with intelligence scores in an independent sample, which exceeds results reported from other modalities. Additionally, we show that tasks that tap executive processing and that are more cognitively demanding are particularly effective for intelligence prediction. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in an activated task state improves brain-based prediction of intelligence.

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.

scholarly journals Large-scale intrinsic connectivity is consistent across varying task demands

2018 ◽  
Author(s):  
Paulina Kieliba ◽  
Sasidhar Madugula ◽  
Nicola Filippini ◽  
Eugene P. Duff ◽  
Tamar R. Makin
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Activation ◽  
Whole Brain ◽  
Activation Patterns ◽  
Intrinsic Connectivity ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

scholarly journals Brain Network Mechanisms of General Intelligence

2019 ◽  
Author(s):  
Chandra Sripada ◽  
Mike Angstadt ◽  
Saige Rutherford ◽  
Aman Taxali
Keyword(s):  
Large Scale ◽  
Brain Network ◽  
Reverse Direction ◽  
General Intelligence ◽  
Activation Patterns ◽  
Human Connectome Project ◽  
Health And Disease ◽  
Lower Intelligence ◽  
Task Conditions ◽  
Intelligence Scores

AbstractWe identify novel mechanisms of general intelligence involving activation patterns of large-scale brain networks. During hard, cognitively demanding tasks, the fronto-parietal network differentially activates relative to the default mode network, creating greater “separation” between the networks, while during easy tasks, network separation is reduced. In 920 adults in the Human Connectome Project dataset, we demonstrate that these network separation patterns across hard and easy task conditions are strongly associated with general intelligence, accounting for 21% of the variance in intelligence scores across individuals. Moreover, we identify the presence of a crossover relationship in which FPN-DMN separation profiles that strongly predict higher intelligence in hard task conditions reverse direction and strongly predict lower intelligence in easy conditions, helping to resolve conflicting findings in the literature. We further clarify key properties of FPN-DMN separation: It is a mediator, and not just a marker, of general intelligence, and FPN-DMN separation profiles during the task state can be reliably predicted from connectivity patterns during rest. We demonstrate the robustness of our results by replicating them in a second task and in an independent large sample of youth. Overall, our results establish FPN-DMN separation as a major locus of individual differences in general intelligence, and raise intriguing new questions about how FPN-DMN separation is regulated in different cognitive tasks, across the lifespan, and in health and disease.

scholarly journals The Domain-General Multiple Demand network is More Active in Early Balanced Bilinguals than Monolinguals During Executive Processing

2021 ◽  
pp. 1-36
Author(s):  
Saima Malik-Moraleda ◽  
Theodor Cucu ◽  
Benjamin Lipkin ◽  
Evelina Fedorenko
Keyword(s):  
Working Memory ◽  
Spatial Working Memory ◽  
Memory Task ◽  
Neural Responses ◽  
Senior Year ◽  
Individual Level ◽  
Executive Processing ◽  
Matched Samples ◽  
The Brain ◽  
Friday Night

Abstract The bilingual experience may place special cognitive demands on speakers and has been argued to lead to improvements in domain-general executive abilities, like cognitive control and working memory. Such improvements have been argued for based on both behavioral and brain imaging evidence. However, the empirical landscape is complex and ridden by controversy. Here we attempt to shed light on this question through an fMRI investigation of relatively large, relatively homogeneous, and carefully matched samples of early balanced bilinguals (n=55) and monolinguals (n=54) using robust, previously validated individual-level markers of neural activity in the domain-general Multiple Demand (MD) network, which supports executive functions. We find that the bilinguals, compared to the monolinguals, show significantly stronger neural responses to an executive (spatial working memory) task, and a larger difference between a harder and an easier condition of the task, across the MD network. These stronger neural responses are accompanied by better behavioral performance on the working memory task. We further show that the bilingual-vs.-monolingual difference in neural responses is not ubiquitous across the brain as no group difference in magnitude is observed in primary visual areas, which also respond to the task. Although the neural group difference in the MD network appears robust, it remains difficult to causally link it to bilingual experience specifically. Dedication: We would like to dedicate this paper to the memory of Albert Costa, who we both knew well and loved as a mentor and a friend. Saima will always be grateful that Albert let her spend her senior year in his lab despite not even being from the same university; his support, mentorship and guidance helped her not stray away from academia when things got tough. And Ev will forever remember the weekly Friday night partying with Albert and the rest of the “crew” in The Cellar and The People’s Republik during her undergrad years in the Caramazza Lab in the late 1990s and early 2000s.

scholarly journals Voxel-based correlation of 18F-THK5351 accumulation and gray matter volume in the brain of cognitively normal older adults

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yoko Shigemoto ◽  
Daichi Sone ◽  
Miho Ota ◽  
Norihide Maikusa ◽  
Masayo Ogawa ◽  
...  
Keyword(s):  
Older Adults ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Cognitively Normal ◽  
Matter Volume ◽  
The Brain

scholarly journals High resolution structural and functional MRI of the hippocampus in young adults with Down syndrome

2021 ◽  
Author(s):  
Katherine A Koenig ◽  
Se-Hong Oh ◽  
Melissa R Stasko ◽  
Elizabeth C Roth ◽  
H Gerry Taylor ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Functional Connectivity ◽  
Cortical Thickness ◽  
Right Hemisphere ◽  
Gray Matter Volume ◽  
Drug Efficacy ◽  
Structure And Function ◽  
7 Tesla ◽  
Whole Brain ◽  
And Function

Abstract Down syndrome is the phenotypic consequence of trisomy 21, with clinical presentation including both neurodevelopmental and neurodegenerative components. Although the intellectual disability typically displayed by individuals with Down syndrome is generally global, it also involves disproportionate deficits in hippocampally-mediated cognitive processes. Hippocampal dysfunction may also relate to Alzheimer’s disease-type pathology, which can appear in as early as the first decade of life and becomes universal by age 40. Using 7-tesla MRI of the brain, we present an assessment of the structure and function of the hippocampus in 34 individuals with Down syndrome (mean age 24.5 years ± 6.5) and 27 age- and sex-matched typically developing healthy controls. In addition to increased whole-brain mean cortical thickness and lateral ventricle volumes (p < 1.0 × 10−4), individuals with Down syndrome showed selective volume reductions in bilateral hippocampal subfields CA1, dentate gyrus, and tail (p < 0.005). In the group with Down syndrome, bilateral hippocampi showed widespread reductions in the strength of functional connectivity, predominately to frontal regions (p < 0.02). Age was not related to hippocampal volumes or functional connectivity measures in either group, but both groups showed similar relationships of age to whole-brain volume measures (p < 0.05). Finally, we performed an exploratory analysis of a subgroup of individuals with Down syndrome with both imaging and neuropsychological assessments. This analysis indicated that measures of spatial memory were related to mean cortical thickness, total gray matter volume, and right hemisphere hippocampal subfield volumes (p < 0.02). This work provides a first demonstration of the usefulness of high-field MRI to detect subtle differences in structure and function of the hippocampus in individuals with Down syndrome, and suggests the potential for development of MRI-derived measures as surrogate markers of drug efficacy in pharmacological studies designed to investigate enhancement of cognitive function.

Individual Uniqueness in the Neonatal Functional Connectome

2021 ◽  
Author(s):  
Qiushi Wang ◽  
Yuehua Xu ◽  
Tengda Zhao ◽  
Zhilei Xu ◽  
Yong He ◽  
...  
Keyword(s):  
Individual Differences ◽  
Individual Identification ◽  
Imaging Data ◽  
Functional Connectome ◽  
Middle Range ◽  
Human Connectome Project ◽  
The Individual ◽  
And Behavior ◽  
Identification Analysis ◽  
The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

Converging Evidence That Neural Plasticity Underlies Transcranial Direct-Current Stimulation

2021 ◽  
Vol 33 (1) ◽  
pp. 146-157
Author(s):  
Chong Zhao ◽  
Geoffrey F. Woodman
Keyword(s):  
Visual Search ◽  
Direct Current ◽  
Neural Plasticity ◽  
Transcranial Direct Current Stimulation ◽  
Time Course ◽  
Memory Task ◽  
Long Term Memory ◽  
Visual Stream ◽  
Direct Current Stimulation ◽  
The Brain

It is not definitely known how direct-current stimulation causes its long-lasting effects. Here, we tested the hypothesis that the long time course of transcranial direct-current stimulation (tDCS) is because of the electrical field increasing the plasticity of the brain tissue. If this is the case, then we should see tDCS effects when humans need to encode information into long-term memory, but not at other times. We tested this hypothesis by delivering tDCS to the ventral visual stream of human participants during different tasks (i.e., recognition memory vs. visual search) and at different times during a memory task. We found that tDCS improved memory encoding, and the neural correlates thereof, but not retrieval. We also found that tDCS did not change the efficiency of information processing during visual search for a certain target object, a task that does not require the formation of new connections in the brain but instead relies on attention and object recognition mechanisms. Thus, our findings support the hypothesis that direct-current stimulation modulates brain activity by changing the underlying plasticity of the tissue.

scholarly journals Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Elaheh Zendehrouh ◽  
Robyn L. Miller ◽  
Zening Fu ◽  
Yuhui Du ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Early Stage ◽  
Network Connectivity ◽  
Functional Network ◽  
Normal Brain ◽  
Whole Brain ◽  
Mild Dementia ◽  
Sensorimotor Network ◽  
Functional Network Connectivity ◽  
The Brain

BackgroundAlzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.MethodWe used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.ResultsAll states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.ConclusionOur results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

scholarly journals Scatterometry Measurements With Scattered Light Imaging Enable New Insights Into the Nerve Fiber Architecture of the Brain

2021 ◽  
Vol 15 ◽  
Author(s):  
Miriam Menzel ◽  
Marouan Ritzkowski ◽  
Jan A. Reuter ◽  
David Gräßel ◽  
Katrin Amunts ◽  
...  
Keyword(s):  
Human Brain ◽  
Nerve Fiber ◽  
Brain Tissue ◽  
Scattered Light ◽  
Polar Angle ◽  
Nerve Fibers ◽  
Fiber Architecture ◽  
Whole Brain ◽  
Tissue Samples ◽  
The Brain

The correct reconstruction of individual (crossing) nerve fibers is a prerequisite when constructing a detailed network model of the brain. The recently developed technique Scattered Light Imaging (SLI) allows the reconstruction of crossing nerve fiber pathways in whole brain tissue samples with micrometer resolution: the individual fiber orientations are determined by illuminating unstained histological brain sections from different directions, measuring the transmitted scattered light under normal incidence, and studying the light intensity profiles of each pixel in the resulting image series. So far, SLI measurements were performed with a fixed polar angle of illumination and a small number of illumination directions, providing only an estimate of the nerve fiber directions and limited information about the underlying tissue structure. Here, we use a display with individually controllable light-emitting diodes to measure the full distribution of scattered light behind the sample (scattering pattern) for each image pixel at once, enabling scatterometry measurements of whole brain tissue samples. We compare our results to coherent Fourier scatterometry (raster-scanning the sample with a non-focused laser beam) and previous SLI measurements with fixed polar angle of illumination, using sections from a vervet monkey brain and human optic tracts. Finally, we present SLI scatterometry measurements of a human brain section with 3 μm in-plane resolution, demonstrating that the technique is a powerful approach to gain new insights into the nerve fiber architecture of the human brain.

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