scholarly journals Structural connectivity changes in the motor execution network after stroke rehabilitation

2021 ◽  
pp. 1-9
Author(s):  
Pradeepa Ruwan Wanni Arachchige ◽  
Sadhani Karunarathna ◽  
Abdul Chalik Meidian ◽  
Ryo Ueda ◽  
Wataru Uchida ◽  
...  
Keyword(s):  
Graph Theory ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Recovery Process ◽  
Brain Regions ◽  
Motor Recovery ◽  
Theory Approach ◽  
Precentral Gyrus ◽  
Motor Execution ◽  
Frontal Pole

Background: Although quite a very few studies have tested structural connectivity changes following an intervention, it reflects only selected key brain regions in the motor network. Thus, the understanding of structural connectivity changes related to the motor recovery process remains unclear. Objective: This study investigated structural connectivity changes of the motor execution network following a combined intervention of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) and intensive occupational therapy (OT) after a stroke using graph theory approach. Methods: Fifty-six stroke patients underwent Fugl-Meyer Assessment (FMA), Wolf Motor Function Test-Functional Ability Scale (WMFT-FAS), diffusion tensor imaging (DTI), and T1 weighted imaging before and after the intervention. We examined graph theory measures related to twenty brain regions using structural connectomes. Results: The ipsilesional and contralesional hemisphere showed structural connectivity changes post-intervention after stroke. We found significantly increased regional centralities and nodal efficiency within the frontal pole and decreased degree centrality and nodal efficiency in the ipsilesional thalamus. Correlations were found between network measures and clinical assessments in the cuneus, postcentral gyrus, precentral gyrus, and putamen of the ipsilesional hemisphere. The contralesional areas such as the caudate, cerebellum, and frontal pole also showed significant correlations. Conclusions: This study was helpful to expand the understanding of structural connectivity changes in both hemispheric networks during the motor recovery process following LF-rTMS and intensive OT after stroke.

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
Author(s):  
Yoed N. Kenett ◽  
Roger E. Beaty ◽  
John D. Medaglia
Keyword(s):  
Control Theory ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Network Control ◽  
Depression Symptoms ◽  
Theory Approach ◽  
Imaging Data ◽  
The Brain ◽  
Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

scholarly journals Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Node Degree ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Structural Brain Network ◽  
Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

scholarly journals Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

2021 ◽  
Author(s):  
David Pascucci ◽  
Maria Rubega ◽  
Joan Rue-Queralt ◽  
Sebastien Tourbier ◽  
Patric Hagmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Meta Analysis ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Noise Perturbation ◽  
Eeg Data ◽  
Functional Brain Networks ◽  
Anatomical Priors ◽  
And Diffusion

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

scholarly journals Connectivity changes in major depressive disorder after rTMS: a review of functional and structural connectivity data

2021 ◽  
Vol 30 ◽  
Author(s):  
G. Schiena ◽  
G. Franco ◽  
A. Boscutti ◽  
G. Delvecchio ◽  
E. Maggioni ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Small Sample Size ◽  
Structural Connectivity ◽  
Unipolar Depression ◽  
Brain Regions ◽  
Small Sample ◽  
Anterior Cingulate ◽  
Antidepressant Effect ◽  
Dorsolateral Prefrontal

Abstract Aims In the search for effective therapeutic strategies for depression, repetitive transcranial magnetic stimulation (rTMS) emerged as a non-invasive, promising treatment. This is because the antidepressant effect of rTMS might be related to neuronal plasticity mechanisms possibly reverting connectivity alterations often observed in depression. Therefore, in this review, we aimed at providing an overview of the findings reported by studies investigating functional and structural connectivity changes after rTMS in depression. Methods A bibliographic search was conducted on PubMed, including studies that used unilateral, excitatory (⩾10 Hz) rTMS treatment targeted on the left dorsolateral prefrontal cortex (DLPFC) in unipolar depressed patients. Results The majority of the results showed significant TMS-induced changes in functional connectivity (FC) between areas important for emotion regulation, including the DLPFC and the subgenual anterior cingulate cortex, and among regions that are part of the major resting-state networks, such as the Default Mode Network, the Salience Networks and the Central Executive Network. Finally, in diffusion tensor imaging studies, it has been reported that rTMS appeared to increase fractional anisotropy in the frontal lobe. Limitations The small sample size, the heterogeneity of the rTMS stimulation parameters, the concomitant use of psychotropic drugs might have limited the generalisability of the results. Conclusions Overall, rTMS treatment induces structural and FC changes in brain regions and networks implicated in the pathogenesis of unipolar depression. However, whether these changes underlie the antidepressant effect of rTMS still needs to be clarified.

scholarly journals Differences in Cortical Representation and Structural Connectivity of Hands and Feet between Professional Handball Players and Ballet Dancers

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Jessica Meier ◽  
Marlene Sofie Topka ◽  
Jürgen Hänggi
Keyword(s):  
Grey Matter ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Voxel Based Morphometry ◽  
Ballet Dancers ◽  
Intensive Training ◽  
Sport Expertise ◽  
Hands And Feet ◽  
Structural Adaptations

It is known that intensive training and expertise are associated with functional and structural neuroadaptations. Most studies, however, compared experts with nonexperts; hence it is, specifically for sports, unclear whether the neuroplastic adaptations reported are sport-specific or sport-general. Here we aimed at investigating sport-specific adaptations in professional handball players and ballet dancers by focusing on the primary motor and somatosensory grey matter (GM) representation of hands and feet using voxel-based morphometry as well as on fractional anisotropy (FA) of the corticospinal tract by means of diffusion tensor imaging-based fibre tractography. As predicted, GM volume was increased in hand areas of handball players, whereas ballet dancers showed increased GM volume in foot areas. Compared to handball players, ballet dancers showed decreased FA in both fibres connecting the foot and hand areas, but they showed lower FA in fibres connecting the foot compared to their hand areas, whereas handball players showed lower FA in fibres connecting the hand compared to their foot areas. Our results suggest that structural adaptations are sport-specific and are manifested in brain regions associated with the neural processing of sport-specific skills. We believe this enriches the plasticity research in general and extends our knowledge of sport expertise in particular.

scholarly journals Identifying structural connectivity priorities in eastern Paraguay’s fragmented Atlantic Forest

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Noé U. de la Sancha ◽  
Sarah A. Boyle ◽  
Nancy E. McIntyre
Keyword(s):  
Graph Theory ◽  
Atlantic Forest ◽  
Spatial Scales ◽  
Structural Connectivity ◽  
Theory Approach ◽  
Stepping Stones ◽  
Conservation Action ◽  
Fragmented Forest ◽  
Forest Remnants ◽  
Entire Network

AbstractThe Atlantic Forest of eastern Paraguay has experienced extensive recent deforestation. Less than one-third of the region is forested, and the remaining forest largely consists of isolated remnants with potentially disrupted connectivity for forest fauna. We used a graph theory approach to identify those forest remnants that are important in maintaining landscape structural connectivity for mammals in this fragmented forest. We quantified structural connectivity for forest remnants over the period 2000–2019 at three levels: the entire network of Atlantic Forest remnants in eastern Paraguay; at 10 smaller, nested spatial scales (40–10,000 m) encompassing a range of potential mammalian dispersal abilities; and at the level of individual remnants. We used 10 graph theory metrics to assess aspects of network complexity, dispersal-route efficiency, and individual remnant importance in supporting structural connectivity. We identified forest remnants that serve as important structural connectivity roles as stepping stones, hubs, or articulation points and that should be prioritized for connectivity conservation. Structural connectivity was constrained for organisms incapable of travelling at least 9–12 km (farthest distances between nearest-neighboring forest remnants depending on whether smaller remnants were included or not) and was particularly limited for area-sensitive forest-specialist mammals. With the increased forest loss and fragmentation that is occurring, the connectivity of this system will likely be further compromised, but most of the remnants that we identified as playing important roles for structural connectivity were outside of the country’s proposed “green corridor,” indicating additional areas where conservation action can be directed.

scholarly journals Altered Volume and Structural Connectivity of the Hippocampus in Alzheimer’s Disease and Amnestic Mild Cognitive Impairment

2021 ◽  
Vol 13 ◽  
Author(s):  
Feng Feng ◽  
Weijie Huang ◽  
Qingqing Meng ◽  
Weijun Hao ◽  
Hongxiang Yao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Hippocampal Volume ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Hippocampal Atrophy

Background: Hippocampal atrophy is a characteristic of Alzheimer’s disease (AD). However, alterations in structural connectivity (number of connecting fibers) between the hippocampus and whole brain regions due to hippocampal atrophy remain largely unknown in AD and its prodromal stage, amnestic mild cognitive impairment (aMCI).Methods: We collected high-resolution structural MRI (sMRI) and diffusion tensor imaging (DTI) data from 36 AD patients, 30 aMCI patients, and 41 normal control (NC) subjects. First, the volume and structural connectivity of the bilateral hippocampi were compared among the three groups. Second, correlations between volume and structural connectivity in the ipsilateral hippocampus were further analyzed. Finally, classification ability by hippocampal volume, its structural connectivity, and their combination were evaluated.Results: Although the volume and structural connectivity of the bilateral hippocampi were decreased in patients with AD and aMCI, only hippocampal volume correlated with neuropsychological test scores. However, positive correlations between hippocampal volume and ipsilateral structural connectivity were displayed in patients with AD and aMCI. Furthermore, classification accuracy (ACC) was higher in AD vs. aMCI and aMCI vs. NC by the combination of hippocampal volume and structural connectivity than by a single parameter. The highest values of the area under the receiver operating characteristic (ROC) curve (AUC) in every two groups were all obtained by combining hippocampal volume and structural connectivity.Conclusions: Our results showed that the combination of hippocampal volume and structural connectivity (number of connecting fibers) is a new perspective for the discrimination of AD and aMCI.

scholarly journals Using Diffusion Tensor Imaging to examine brain structural plasticity and language experience

2019 ◽  
Author(s):  
Gigi Luk ◽  
Christos Pliatsikas
Keyword(s):  
Language Processing ◽  
Brain Structure ◽  
Language Use ◽  
Diffusion Tensor ◽  
Brain Plasticity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Language Experience ◽  
Brain Correlates ◽  
The Brain

Recent advances in neuroimaging methods have led to a renewed interest in the brain correlates of language processing. Most intriguing is how experiences of language use relates to variation in brain structure and how brain structure predicts language acquisition. These two lines of inquiry have important implications on considering language use as an experience-dependent mechanism that induces brain plasticity. This paper focuses on the structural connectivity of the brain, as delivered by white matter, i.e. the collections of the axons of the brain neurons that provide connectivity between brain regions. Tract-Based Spatial Statistics (TBSS), a method commonly used in the field, will be presented in detail. Readers will be introduced to procedures for the extraction of indices of variation in WM structure such as fractional anisotropy. Furthermore, the role of individual differences in WM and changes in WM pertaining to bilingual experience and language processing will be used as examples to illustrate the applicability of this method.

scholarly journals The within-subject application of diffusion tensor MRI and CLARITY reveals brain structural changes in Nrxn2 deletion mice

2018 ◽  
Author(s):  
Eleftheria Pervolaraki ◽  
Adam L. Tyson ◽  
Francesca Pibiri ◽  
Steven L. Poulter ◽  
Amy C. Reichelt ◽  
...  
Keyword(s):  
Anterior Cingulate Cortex ◽  
Orbitofrontal Cortex ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Cingulate Cortex ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Anterior Cingulate ◽  
Diffusion Tensor Mri ◽  
Socially Relevant

AbstractBackgroundOf the many genetic mutations known to increase the risk of autism spectrum disorder, a large proportion cluster upon synaptic proteins. One such family of presynaptic proteins are the neurexins (NRXN), and recent genetic and mouse evidence has suggested a causative role for NRXN2 in generating altered social behaviours. Autism has been conceptualised as a disorder of atypical connectivity, yet how single-gene mutations affect such connectivity remains under-explored. To attempt to address this, we have developed a quantitative analysis of microstructure and structural connectivity leveraging diffusion tensor MRI (DTI) with high-resolution 3D imaging in optically cleared (CLARITY) brain tissue in the same mouse, applied here to the Nrxn2α knockout (KO) model.MethodsFixed brains of Nrxn2α KO mice underwent DTI using 9.4T MRI, and diffusion properties of socially-relevant brain regions were quantified. The same tissue was then subjected to CLARITY to immunolabel axons and cell bodies, which were also quantified.ResultsDTI revealed decreases in fractional anisotropy and increases in apparent diffusion coefficient in the amygdala (including the basolateral nuclei), the anterior cingulate cortex, the orbitofrontal cortex and the hippocampus. Radial diffusivity of the anterior cingulate cortex and orbitofrontal cortex was significantly increased in Nrxn2α KO mice, as were tracts between the amygdala and the orbitofrontal cortex. Using CLARITY, we find significantly altered axonal orientation in the amygdala, orbitofrontal cortex and the anterior cingulate cortex, which was unrelated to cell density.ConclusionsOur findings demonstrate that deleting a single neurexin gene (Nrxn2α) induces atypical structural connectivity within socially-relevant brain regions. More generally, our combined within-subject DTI and CLARITY approach presents a new, more sensitive method of revealing hitherto undetectable differences in the autistic brain.

scholarly journals Large-scale exploration of whole-brain structural connectivity in anorexia nervosa: alterations in the connectivity of frontal and subcortical networks

2021 ◽  
Author(s):  
E. Caitlin Lloyd ◽  
Karin E. Foerde ◽  
Alexandra F. Muratore ◽  
Natalie Aw ◽  
David Semanek ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Graph Theory ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Cross Sectional ◽  
Whole Brain ◽  
Subcortical Regions

AbstractBackgroundAnorexia nervosa (AN) is characterised by disturbances in cognition and behaviour surrounding eating and weight, which may relate to the structural connectivity of the brain that supports effective information processing and transfer.MethodsDiffusion-weighted MRI data acquired from female patients with AN (n = 148) and female healthy controls (HC; n = 119), aged 12-40 years, were combined across five cross-sectional studies. Probabilistic tractography was completed, and full cortex connectomes describing streamline counts between 84 brain regions generated and harmonised. The network-based statistic tested between-group differences in connectivity strength of brain subnetworks. Whole-brain connectivity of brain regions was indexed using graph theory tools, and compared between groups using multiple linear regression. Associations between structural connectivity variables that differed between groups, and illness severity markers, were explored amongst AN patients using multiple linear regression. Statistical models included age, motion, and study as covariates.OutcomesThe network-based statistic indicated AN patients, relative to HC, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < 0.05, FWE corrected). Graph theory analyses supported reduced connectivity of subcortical regions, and greater connectivity of left occipital cortex, in patients relative to HC (p < 0.05, permutation corrected). Reduced subcortical network connectivity was associated with lower BMI among the AN group.InterpretationStructural differences in subcortical and cortical networks are present in AN, and may reflect illness mechanisms.FundingGlobal Foundation for Eating Disorders; Klarman Family Foundation; Translating Duke Health Initiative; NIMH (MH099388, MH076195, MH110445, MH105452, MH079397, MH113737).

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