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•Hippocampal-cortical connectivity gradients change during naturalistic memory tasks.•Familiar cues accentuate the hippocampal anterior-posterior functional transition.•This transition is more posterior in the left hippocampus of subjects with MCI or AD.•Gradients of the anterior hippocampus map onto the default mode network. The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.

Simultaneous multi‐slice (multiband) acceleration in fMRI has become widespread, but may be affected by novel forms of signal artifact. Here, we demonstrate a previously unreported artifact manifesting as a shared signal between simultaneously acquired slices in all resting‐state and task‐based multiband fMRI datasets we investigated, including publicly available consortium data from the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) Study. We propose Multiband Artifact Regression in Simultaneous Slices (MARSS), a regression‐based detection and correction technique that successfully mitigates this shared signal in unprocessed data. We demonstrate that the signal isolated by MARSS correction is likely nonneural, appearing stronger in neurovasculature than gray matter. Additionally, we evaluate MARSS both against and in tandem with sICA+FIX denoising, which is implemented in HCP resting‐state data, to show that MARSS mitigates residual artifact signal that is not modeled by sICA+FIX. MARSS correction leads to study‐wide increases in signal‐to‐noise ratio, decreases in cortical coefficient of variation, and mitigation of systematic artefactual spatial patterns in participant‐level task betas. Finally, MARSS correction has substantive effects on second‐level t‐statistics in analyses of task‐evoked activation. We recommend that investigators apply MARSS to multiband fMRI datasets with moderate or higher acceleration factors, in combination with established denoising methods. We present a previously unreported artifact signal shared between simultaneously acquired slices in resting‐state and task‐based multiband fMRI. We developed Multiband Artifact Regression in Simultaneous Slices (MARSS) to estimate and remove this artifact, leading to improved data quality and substantive changes to task‐based fMRI analysis results within and across participants.

Executive functions, the set of cognitive control processes that facilitate adaptive thoughts and actions, are composed primarily of three distinct yet interrelated cognitive components: Inhibition, Shifting, and Updating. While prior brain research has examined the nature of different components as well as their interrelationships, fewer studies examined whole‐brain connectivity to predict individual differences for the three cognitive components and associated task scores. Here, using the Connectome‐based Predictive Modelling (CPM) approach and open‐access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2‐back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2‐back tasks after confirming superior predictive performance over resting‐state and volumetric data. High cross‐task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default‐mode networks, suggest the existence of a common executive function factor. To investigate the relationships among the three executive function components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared executive function component can be predicted from functional connectivity patterns densely located around the frontoparietal, default‐mode, and dorsal attention networks. The Updating‐specific component showed significant cross‐prediction with the general executive function factor, suggesting a relatively stronger role than the other components. In contrast, the Shifting‐specific and Inhibition‐specific components exhibited lower cross‐prediction accuracy, indicating more distinct and specialized roles. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates a novel approach to infer common and specific components of executive function. Using connectome‐based modeling, we predict novel individuals' executive function (EF) performance and characterize the functional networks underlying specific EF components—inhibition, shifting, and updating—as well as a general EF factor shared across them.

Background Motor imagery training (MIT) has been widely used to improve hemiplegic upper limb function in stroke rehabilitation. The effectiveness of MIT is associated with the functional neuroplasticity of the motor network. Currently, brain activation and connectivity changes related to the motor recovery process after MIT are not well understood. Aim: We aimed to investigate the neural mechanisms of MIT in stroke rehabilitation through a longitudinal intervention study design with task‐based functional magnetic resonance imaging (fMRI) analysis. Methods We recruited 39 stroke patients with moderate to severe upper limb motor impairment and randomly assigned them to either the MIT or control groups. Patients in the MIT group received 4 weeks of MIT therapy plus conventional rehabilitation, while the control group only received conventional rehabilitation. The assessment of Fugl‐Meyer Upper Limb Scale (FM‐UL) and Barthel Index (BI), and fMRI scanning using a passive hand movement task were conducted on all patients before and after treatment. The changes in brain activation and functional connectivity (FC) were analyzed. Pearson's correlation analysis was conducted to evaluate the association between neural functional changes and motor improvement. Results The MIT group achieved higher improvements in FM‐UL and BI relative to the control group after the treatment. Passive movement of the affected hand evoked an abnormal bilateral activation pattern in both groups before intervention. A significant Group × Time interaction was found in the contralesional S1 and ipsilesional M1, showing a decrease of activation after intervention specifically in the MIT group, which was negatively correlated with the FM‐UL improvement. FC analysis of the ipsilesional M1 displayed the motor network reorganization within the ipsilesional hemisphere, which correlated with the motor score changes. Conclusions MIT could help decrease the compensatory activation at both hemispheres and reshape the FC within the ipsilesional hemisphere along with functional recovery in stroke patients. The neural mechanism of motor imagery training in stroke patients with moderate to severe upper limb motor impairments was investigated using task‐based fMRI. The results revealed that motor imagery training during motor recovery decreased overaction at both hemispheres and reorganized the motor network within the ipsilesional hemisphere.

Many recent studies have revealed that spatial interactions of functional brain networks derived from fMRI data can well model functional connectomes of the human brain. However, it has been rarely explored what the energy consumption characteristics are for such spatial interactions of macro‐scale functional networks, which remains crucial for the understanding of brain organization, behavior, and dynamics. To explore this unanswered question, this article presents a novel framework for quantitative assessment of energy consumptions of macro‐scale functional brain network's spatial interactions via two main effective computational methodologies. First, we designed a novel scheme combining dictionary learning and hierarchical clustering to derive macro‐scale consistent brain network templates that can be used to define a common reference space for brain network interactions and energy assessments. Second, the control energy consumption for driving the brain networks during their spatial interactions is computed from the viewpoint of the linear network control theory. Especially, the energetically favorable brain networks were identified and their energy characteristics were comprehensively analyzed. Experimental results on the Human Connectome Project (HCP) task‐based fMRI (tfMRI) data showed that the proposed methods can reveal meaningful, diverse energy consumption patterns of macro‐scale network interactions. In particular, those networks present remarkable differences in energy consumption. The energetically least favorable brain networks are stable and consistent across HCP tasks such as motor, language, social, and working memory tasks. In general, our framework provides a new perspective to characterize human brain functional connectomes by quantitative assessment for the energy consumption of spatial interactions of macro‐scale brain networks. The article focuses on quantitative assessment of energy consumptions of macro‐scale functional brain network's spatial interactions. Meaningful, diverse energy consumption patterns of macro‐scale network interactions are revealed. In particular, the energetically least favorable brain networks are stable and consistent across different HCP tasks.

The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels. •Describes logic for the behavioral battery for the Human Connectome Project (HCP)•Describes logic and development of the task fMRI (tfMRI) battery for the HCP•Provides data on brain activation associated with each tfMRI paradigm in the HCP

•Confidence Sets (CSs) extend the idea of confidence intervals to fMRI maps.•For a Cohen’s d threshold c, upper CS asserts where d>c, lower CS where d

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