scholarly journals Network Localization of Executive Function Deficits in Patients with Focal Thalamic Lesions

2020 ◽  
Vol 32 (12) ◽  
pp. 2303-2319
Author(s):  
Kai Hwang ◽  
Joel Bruss ◽  
Daniel Tranel ◽  
Aaron D. Boes
Keyword(s):  
Executive Function ◽  
Functional Connectivity ◽  
Brain Injuries ◽  
Functional Neuroimaging ◽  
Executive Dysfunction ◽  
Brain Regions ◽  
Functional Networks ◽  
Thalamic Lesion ◽  
Human Thalamus ◽  
Thalamic Lesions

The human thalamus has been suggested to be involved in executive function, based on animal studies and correlational evidence from functional neuroimaging in humans. Human lesion studies, examining behavioral deficits associated with focal brain injuries, can directly test the necessity of the human thalamus for executive function. The goal of our study was to determine the specific lesion location within the thalamus as well as the potential disruption of specific thalamocortical functional networks, related to executive dysfunction. We assessed executive function in 15 patients with focal thalamic lesions and 34 comparison patients with lesions that spared the thalamus. We found that patients with mediodorsal thalamic lesions exhibited more severe impairment in executive function when compared to both patients with thalamic lesions that spared the mediodorsal nucleus and to comparison patients with lesions outside the thalamus. Furthermore, we employed a lesion network mapping approach to map cortical regions that show strong functional connectivity with the lesioned thalamic subregions in the normative functional connectome. We found that thalamic lesion sites associated with more severe deficits in executive function showed stronger functional connectivity with ACC, dorsomedial PFC, and frontoparietal network, compared to thalamic lesions not associated with executive dysfunction. These are brain regions and functional networks whose dysfunction could contribute to impaired executive functioning. In aggregate, our findings provide new evidence that delineates a thalamocortical network for executive function.

scholarly journals Failure of activation of striatum during the performance of executive function tasks in adult patients with bipolar disorder

2019 ◽  
Vol 50 (4) ◽  
pp. 653-665 ◽  
Author(s):  
Fangfang Tian ◽  
Wei Diao ◽  
Xun Yang ◽  
Xiuli Wang ◽  
Neil Roberts ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Executive Function ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Executive Dysfunction ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Precentral Gyrus ◽  
Web Based ◽  
Reliable Marker

AbstractBackgroundAlthough numerous studies have used functional neuroimaging to identify executive dysfunction in patients with bipolar disorder (BD), the findings are not consistent. The aim of this meta-analysis is to identify the most reliable functional anomalies in BD patients during performance of Executive Function (EF) tasks.MethodsA web-based search was performed on publication databases to identify functional magnetic resonance imaging studies of BD patients performing EF tasks and a voxel-based meta-analytic method known as anisotropic Effect Size Signed Differential Mapping (ES-SDM) was used to identify brain regions which showed anomalous activity in BD patients compared with healthy controls (HC).ResultsTwenty datasets consisting of 463 BD patients and 484 HC were included. Compared with HC, BD patients showed significant hypo-activation or failure of activation in the left striatum (p = 0.00007), supplementary motor area (BA 6, p = 0.00037), precentral gyrus (BA 6, p = 0.0014) and cerebellum (BA 37, p = 0.0019), and hyper-activation in the left gyrus rectus (BA 11, p ≈ 0) and right middle temporal gyrus (BA 22, p = 0.00031) during performance of EF tasks. Sensitivity and subgroup analyses showed that the anomaly of left striatum is consistent across studies and present in both euthymic and BD I patients.ConclusionsPatients with BD consistently showed abnormal activation in the cortico-striatal system during performance of EF tasks compared with HC. Failure of activation of the striatum may be a reliable marker for impairment in performance of especially inhibition tasks by patients with BD.

scholarly journals Chemotherapy Pharmacodynamics and Neuroimaging and Neurocognitive Outcomes in Long-Term Survivors of Childhood Acute Lymphoblastic Leukemia

2016 ◽  
Vol 34 (22) ◽  
pp. 2644-2653 ◽  
Author(s):  
Kevin R. Krull ◽  
Yin Ting Cheung ◽  
Wei Liu ◽  
Slim Fellah ◽  
Wilburn E. Reddick ◽  
...  
Keyword(s):  
Executive Function ◽  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Executive Dysfunction ◽  
Brain Regions ◽  
Childhood Acute Lymphoblastic Leukemia ◽  
Long Term Follow Up ◽  
Long Term Survivors

Purpose To examine associations among methotrexate pharmacodynamics, neuroimaging, and neurocognitive outcomes in long-term survivors of childhood acute lymphoblastic leukemia treated on a contemporary chemotherapy-only protocol. Patients and Methods This longitudinal study linked pharmacokinetic assays collected during therapy to neurocognitive and brain imaging outcomes during long-term follow-up. A total of 218 (72.2%) of 302 eligible long-term survivors were recruited for outcome studies when they were more than 5 years post-diagnosis and older than 8 years of age. At long-term follow-up, survivors were an average of 13.8 years old and 7.7 years from diagnosis, and 51% were male. Neurocognitive testing, functional magnetic resonance imaging (MRI) during an executive function task, and structural MRI with diffusion tensor imaging were conducted. Generalized linear models were developed to identify predictors, and models were adjusted for age at diagnosis, sex, and parent education. Results Intelligence was within normal limits (mean, 98; standard deviation, 14) compared with population expectations (mean, 100; standard deviation, 15), though measures of executive function, processing speed, and memory were less than population means (all P < .02 after correction for false discovery rates). Higher plasma concentration of methotrexate was associated with a poorer executive function score (P < .02). Higher plasma methotrexate was also associated with higher functional MRI activity, with thicker cortices in dorsolateral prefrontal brain regions, and with white matter microstructure in the frontostriatal tact. Neurocognitive impairment was associated with these imaging findings as well. Associations did not change after adjustment for age or dose of leucovorin rescue. Conclusion Survivors of childhood acute lymphoblastic leukemia treated on contemporary chemotherapy-only protocols demonstrate executive dysfunction. A higher plasma concentration of methotrexate was associated with executive dysfunction as well as with a thicker cortex and higher activity in frontal brain regions, regions often associated with executive function.

scholarly journals Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics

2021 ◽  
Vol 118 (46) ◽  
pp. e2109380118
Author(s):  
Maria Pope ◽  
Makoto Fukushima ◽  
Richard F. Betzel ◽  
Olaf Sporns
Keyword(s):  
Functional Connectivity ◽  
Functional Neuroimaging ◽  
Empirical Studies ◽  
Structural Connectivity ◽  
Spatial Arrangement ◽  
High Amplitude ◽  
Brain Regions ◽  
Brain Dynamics ◽  
Modular Organization ◽  
Fine Scale

The topology of structural brain networks shapes brain dynamics, including the correlation structure of brain activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that functional connectivity fluctuates over time, exhibiting patterns that vary in the spatial arrangement of correlations among segregated functional systems. Recently, an exact decomposition of functional connectivity into frame-wise contributions has revealed fine-scale dynamics that are punctuated by brief and intermittent episodes (events) of high-amplitude cofluctuations involving large sets of brain regions. Their origin is currently unclear. Here, we demonstrate that similar episodes readily appear in silico using computational simulations of whole-brain dynamics. As in empirical data, simulated events contribute disproportionately to long-time functional connectivity, involve recurrence of patterned cofluctuations, and can be clustered into distinct families. Importantly, comparison of event-related patterns of cofluctuations to underlying patterns of structural connectivity reveals that modular organization present in the coupling matrix shapes patterns of event-related cofluctuations. Our work suggests that brief, intermittent events in functional dynamics are partly shaped by modular organization of structural connectivity.

scholarly journals Functionnectome: a framework to analyse the contribution of brain circuits to fMRI

2021 ◽  
Author(s):  
Victor Nozais ◽  
Stephanie Forkel ◽  
Chris Foulon ◽  
Laurent Petit ◽  
Michel Thiebaut de Schotten
Keyword(s):  
White Matter ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Functional Networks ◽  
Language Functions ◽  
Brain Circuits ◽  
Novel Method ◽  
Joint Contribution ◽  
The Relationship ◽  
The Brain

Abstract In recent years, the field of functional neuroimaging has moved from a pure localisationist approach of isolated functional brain regions to a more integrated view of those regions within functional networks. The methods used to investigate such networks, however, rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel forward our understanding of the brain’s functional signatures and dysfunctions. We developed a novel method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionectome combines the functional signal from fMRI with the anatomy of white matter brain circuits to unlock and chart the first maps of functional white matter. To showcase the versatility of this new method, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open source companion software and opens new avenues into studying functional networks by applying the method to already existing dataset and beyond task fMRI.

scholarly journals Comparing the functional structure of neural networks from representational similarity analysis with those from functional connectivity and univariate analyses

2018 ◽  
Author(s):  
Ineke Pillet ◽  
Hans Op de Beeck ◽  
Haemy Lee Masson
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Univariate Analysis ◽  
Brain Regions ◽  
Functional Structure ◽  
Functional Networks ◽  
Similarity Analysis ◽  
Network Nodes ◽  
Representational Similarity Analysis ◽  
The Neural Network

AbstractThe invention of representational similarity analysis (RSA, following multi-voxel pattern analysis (MVPA)) has allowed cognitive neuroscientists to identify the representational structure of multiple brain regions, moving beyond functional localization. By comparing these structures, cognitive neuroscientists can characterize how brain areas form functional networks. Univariate analysis (UNIVAR) and functional connectivity analysis (FCA) are two other popular methods to identify the functional structure of brain networks. Despite their popularity, few studies have examined the relationship between the structure of the networks from RSA with those from UNIVAR and FCA. Thus, the aim of the current study is to examine the similarities between neural networks derived from RSA with those from UNIVAR and FCA to explore how these methods relate to each other. We analyzed the data of a previously published study with the three methods and compared the results by performing (partial) correlation and multiple regression analysis. Our findings reveal that neural networks resulting from RSA, UNIVAR, and FCA methods are highly similar to each other even after ruling out the effect of anatomical proximity between the network nodes. Nevertheless, the neural network from each method shows idiosyncratic structure that cannot be explained by any of the other methods. Thus, we conclude that the RSA, UNIVAR and FCA methods provide similar but not identical information on how brain regions are organized in functional networks.

scholarly journals Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

2020 ◽  
Vol 10 (11) ◽  
pp. 777
Author(s):  
Nicholas John Simos ◽  
Stavros I. Dimitriadis ◽  
Eleftherios Kavroulakis ◽  
Georgios C. Manikis ◽  
George Bertsias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Lupus Erythematosus ◽  
Statistical Power ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Brain Network ◽  
Functional Brain ◽  
Graph Metrics ◽  
Functional Brain Network

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

scholarly journals Multi-Modal Analysis of Resting-State fMRI Data in mTBI Patients and Association With Neuropsychological Outcomes

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian Shi ◽  
Jing Teng ◽  
Xianping Du ◽  
Na Li
Keyword(s):  
Correlation Analysis ◽  
Resting State ◽  
Brain Injuries ◽  
Structural Connectivity ◽  
Executive Dysfunction ◽  
Brain Regions ◽  
Acute Stage ◽  
Anterior Cingulate ◽  
Control Group ◽  
Neuropsychological Outcomes

Various cognitive disorders have been reported for mild traumatic brain injury (mTBI) patients during the acute stage. This acute stage provides an opportunity for clinicians to optimize treatment protocols, which are based on the evaluation of brain structural connectivity. So far, most brain functional magnetic resonance imaging studies are focused on moderate to severe traumatic brain injuries (TBIs). In this study, we prospectively collected resting state data on 50 mTBI within 3 days of injury and 50 healthy volunteers and analyzed them using Amplitude of low-frequency fluctuation (ALFF), Regional Homogeneity (ReHo), graph theory methods and behavior measure, to explore the dysfunctional brain regions in acute mTBI. In our study, a total of 50 patients suffering &lt;3 days mTBI and 50 healthy subjects were tested in rs-fMRI, as well as under neuropsychological examinations including the Wechsler Intelligence Scale and Stroop Color and Word Test. The correlation analysis was conducted between graph theoretic parameters and neuropsychological results. For the mTBI group, the ReHo of the inferior temporal gyrus and the cerebellum superior are significantly lower than in the control group, and the ALFF of the left insula, the cerebellum inferior, and the middle occipital gyrus were significantly higher than in the control group, which implies the dysfunctionality usually observed in Parkinson's disease. Executive function disorder was significantly correlated with the global efficiencies of the dorsolateral superior frontal gyrus and the anterior cingulate cortex, which is consistent with the literature: the acute mTBI patients demonstrate abnormality in terms of motor speed, association, information processing speed, attention, and short-term memory function. Correlation analysis between the neuropsychological outcomes and the network efficiency for the mTBI group indicates that executive dysfunction might be caused by local brain changes. Our data support the idea that the cerebral internal network has compensatory reactions in response to sudden pathological and neurophysiological changes. In the future, multimode rs-fMRI analysis could be a valuable tool for evaluating dysfunctional brain regions after mTBI.

scholarly journals Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder

2021 ◽  
Author(s):  
Shreyas Harita ◽  
Davide Momi ◽  
Frank Mazza ◽  
John David Griffiths
Keyword(s):  
Major Depressive Disorder ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Brain Regions ◽  
Functional Networks ◽  
Major Depressive ◽  
Default Mode ◽  
Brain Physiology ◽  
Subject Variability

Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The effects of TMS on both brain physiology and therapeutic outcomes are known to be highly variable from subject to subject, however. Proposed reasons for this variability include individual differences in neurophysiology, in cortical geometry, and in brain connectivity. Standard approaches to TMS target site definition tend to focus on coordinates or landmarks within the individual brain regions implicated in MDD, such as the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC). Additionally considering the network connectivity of these sites (i.e. the wider set of brain regions that may be mono- or poly-synaptically activated by TMS stimulation) has the potential to improve subject-specificity of TMS targeting and, in turn, improve treatment outcomes. In this study, we looked at the functional connectivity (FC) of dlPFC and OFC TMS targets, based on induced electrical field (E-field) maps, estimated using the SimNIBS library. We hypothesized that individual differences in spontaneous functional brain dynamics would contribute more to downstream network engagement than individual differences in cortical geometry (i.e., E-field variability). We generated individualized E-field maps on the cortical surface for 12 subjects (4 female) from the Human Connectome Project database using tetrahedral head models generated from T1-weighted MR images. F3 and Fp1 electrode positions were used to target the left dlPFC and left OFC, respectively. We analyzed inter-subject variability in the shape and location of these TMS target E-field patterns, their FC, and the major functional networks to which they belong. Our results revealed the key differences in TMS target FC between the dlPFC and OFC, and also how this connectivity varies across subjects. Three major functional networks were targeted across the dlPFC and OFC: the ventral attention, fronto-parietal and default-mode networks in the dlPFC, and the fronto-parietal and default mode networks in the OFC. Inter-subject variability in cortical geometry and in FC was high. Our analyses showed that use of normative neuroimaging reference data (group-average or representative FC and subject E-field) allow prediction of which networks are targeted, but fail to accurately quantify the relative loading of TMS targeting on each of the principal networks. Our results characterize the FC patterns of canonical therapeutic TMS targets, and the key dimensions of their variability across subjects. The high inter-individual variability in cortical geometry and FC, leading to high variability in distributions of targeted brain networks, may account for the high levels of variability in physiological and therapeutic TMS outcomes. These insights should, we hope, prove useful as part of the broader effort by the psychiatry, neurology, and neuroimaging communities to help improve and refine TMS therapy, through a better understanding of the technology and its neurophysiological effects.

scholarly journals Functionnectome: a framework to analyse the contribution of brain circuits to fMRI

2021 ◽  
Author(s):  
Victor Nozais ◽  
Stephanie J Forkel ◽  
Chris J Foulon ◽  
Laurent Petit ◽  
Michel Thiebaut de Schotten
Keyword(s):  
White Matter ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Functional Networks ◽  
Language Functions ◽  
Brain Circuits ◽  
Novel Method ◽  
Joint Contribution ◽  
The Relationship ◽  
The Brain

In recent years, the field of functional neuroimaging has moved from a pure localisationist approach of isolated functional brain regions to a more integrated view of those regions within functional networks. The methods used to investigate such networks, however, rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel forward our understanding of the brain's functional signatures and dysfunctions. We developed a novel method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionectome combines the functional signal from fMRI with the anatomy of white matter brain circuits to unlock and chart the first maps of functional white matter. To showcase the versatility of this new method, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open-source companion software and opens new avenues into studying functional networks by applying the method to already existing dataset and beyond task fMRI.

scholarly journals Functionnectome as a framework to analyse the contribution of brain circuits to fMRI

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Victor Nozais ◽  
Stephanie J. Forkel ◽  
Chris Foulon ◽  
Laurent Petit ◽  
Michel Thiebaut de Schotten
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Functional Networks ◽  
Language Functions ◽  
Brain Circuits ◽  
Joint Contribution ◽  
The Relationship ◽  
The Brain

AbstractIn recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel our understanding of the brain’s functional signatures and dysfunctions. We developed a method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionnectome combines the functional signal from fMRI with white matter circuits’ anatomy to unlock and chart the first maps of functional white matter. To showcase this method’s versatility, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open-source companion software and opens new avenues into studying functional networks by applying the method to already existing datasets and beyond task fMRI.

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