scholarly journals Independent contributions of structural and functional connectivity: evidence from a stroke model

2021 ◽  
pp. 1-45
Author(s):  
Lynsey M Keator ◽  
Grigori Yourganov ◽  
Alexandra Basilakos ◽  
Argye E Hillis ◽  
Gregory Hickok ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Left Hemisphere ◽  
Language Impairment ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Ventral Stream ◽  
Speech Comprehension ◽  
Resting State Functional Connectivity ◽  
Stream Network ◽  
Functional Connections

Abstract Altered functional connectivity is related to severity of language impairment in poststroke aphasia. However, it is not clear whether this finding specifically reflects loss of functional coherence, or more generally, is related to decreased structural connectivity due to cortical necrosis. The aim of the current study was to investigate this issue by factoring out structural connectivity from functional connectivity measures and then relating the residual data to language performance post-stroke. Ninety-seven participants with a history of stroke were assessed using language impairment measures (Auditory Verbal Comprehension and Spontaneous Speech scores from the Western Aphasia Battery-Revised) and MRI (structural, diffusion tensor imaging, and resting state functional connectivity). We analyzed the association between functional connectivity and language and controlled for multiple potential neuroanatomical confounders, namely structural connectivity. We identified functional connections within the left-hemisphere ventral stream where decreased functional connectivity, independent of structural connectivity, was associated with speech comprehension impairment. These connections exist in frontotemporal andtemporoparietal regions. Our results suggest poor speech comprehension in aphasia is at least partially caused by loss of cortical synchrony in a left hemisphere ventral stream network and is not only reflective of localized necrosis or structural connectivity.

scholarly journals Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

2022 ◽  
Author(s):  
Matthew J. Hoptman ◽  
Umit Tural ◽  
Kelvin O. Lim ◽  
Daniel C. Javitt ◽  
Lauren E. Oberlin
Keyword(s):  
Diffusion Tensor Imaging ◽  
Functional Connectivity ◽  
Resting State ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Resting State Functional Connectivity ◽  
Syndrome Scale ◽  
Healthy Control ◽  
Negative Syndrome

Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects and acquired resting state fMRI and diffusion tensor imaging. The Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) was used to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal components analysis. Nine of 28 possible region pairs showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS values correlated with several aspects of the Positive and Negative Syndrome Scale, positively with those involving inwardly directed psychopathology, and negatively with those involving external psychopathology. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.

scholarly journals Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

2021 ◽  
Author(s):  
Matthew J. Hoptman ◽  
Umit Tural ◽  
Kelvin O. Lim ◽  
Daniel C. Javitt ◽  
Lauren E. Oberlin
Keyword(s):  
Diffusion Tensor Imaging ◽  
Functional Connectivity ◽  
Resting State ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Resting State Functional Connectivity ◽  
Syndrome Scale ◽  
Healthy Control ◽  
Negative Syndrome

Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects and acquired resting state fMRI and diffusion tensor imaging. The Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) was used to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal components analysis. Nine of 28 possible region pairs showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS values correlated with several aspects of the Positive and Negative Syndrome Scale, positively with those involving inwardly directed psychopathology, and negatively with those involving external psychopathology. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.

scholarly journals Altered Thalamocortical Connectivity in Six-Week Old Infants at High Familial Risk for Autism Spectrum Disorder

2020 ◽  
Author(s):  
Aarti Nair ◽  
Rhideeta Jalal ◽  
Janelle Liu ◽  
Tawny Tsang ◽  
Nicole M. McDonald ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Familial Risk ◽  
Structural Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Structural Level ◽  
Resting State Functional Connectivity ◽  
Adults With Autism

ABSTRACTConverging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and lowrisk controls (LR). Resting-state functional connectivity (rs-fcMRI) and diffusion tensor imaging (DTI) data were acquired 52 six-week-old infants. Functional connectivity was examined between six cortical seeds –prefrontal, motor, somatosensory, temporal, parietal, and occipital regions– and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared to LR infants. Notably, aberrant rs-fcMRI and DTI connectivity indices at 6 weeks predicted atypical social development between 6 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as six weeks of age, providing a possible early marker of risk for ASD.

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 Joint estimation of neural sources and their functional connections from MEG data

2020 ◽  
Author(s):  
Narayan Puthanmadam Subramaniyam ◽  
Filip Tronarp ◽  
Simo Särkkä ◽  
Lauri Parkkonen
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Structural Connectivity ◽  
Identification Problem ◽  
Joint Estimation ◽  
Bayesian Filtering ◽  
Sequential Approach ◽  
Connectivity Structure ◽  
Saem Algorithm ◽  
Functional Connections

AbstractCurrent techniques to estimate directed functional connectivity from magnetoencephalography (MEG) signals involve two sequential steps; 1) Estimation of the sources and their amplitude time series from the MEG data by solving the inverse problem, and 2) fitting a multivariate autoregressive (MVAR) model to these time series for the estimation of AR coefficients, which reflect the directed interactions between the sources. However, such a sequential approach is not optimal since i) source estimation algorithms typically assume that the sources are independent, ii) the information provided by the connectivity structure is not used to inform the estimation of source amplitudes, and iii) the limited spatial resolution of source estimates often leads to spurious connectivity due to spatial leakage.Here, we present an algorithm to jointly estimate the source and connectivity parameters using Bayesian filtering, which does not require anatomical constraints in form of structural connectivity or a-priori specified regions-of-interest. By formulating a state-space model for the locations and amplitudes of a given number of sources, we show that estimation of functional connectivity can be reduced to a system identification problem. We derive a solution to this problem using a variant of the expectation–maximization (EM) algorithm known as stochastic approximation EM (SAEM).Compared to the traditional two-step approach, the joint approach using the SAEM algorithm provides a more accurate reconstruction of connectivity parameters, which we show with a connectivity benchmark simulation as well as with an electrocorticography-based simulation of MEG data. Using real MEG responses to visually presented faces in 16 subjects, we also demonstrate that our method gives source and connectivity estimates that are both physiologically plausible and largely consistent across subjects. In conclusion, the proposed joint-estimation approach based on the SAEM algorithm outperforms the traditional two-step approach in determining functional connectivity structure in MEG data.

scholarly journals Alterations of the Brain Microstructure and Corresponding Functional Connectivity in Early-Blind Adolescents

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Zhifeng Zhou ◽  
Jinping Xu ◽  
Leilei Shi ◽  
Xia Liu ◽  
Fen Hou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Region Of Interest ◽  
Visual Deprivation ◽  
Light Perception ◽  
Resting State Functional Connectivity ◽  
Structural Reorganization ◽  
Brain Microstructure ◽  
The Brain

Although evidence from studies on blind adults indicates that visual deprivation early in life leads to structural and functional disruption and reorganization of the brain, whether young blind people show similar patterns remains unknown. Therefore, this study is aimed at exploring the structural and functional alterations of the brain of early-blind adolescents (EBAs) compared to normal-sighted controls (NSCs) and investigating the effects of residual light perception on brain microstructure and function in EBAs. We obtained magnetic resonance imaging (MRI) data from 23 EBAs (8 with residual light perception (LPs), 15 without light perception (NLPs)) and 21 NSCs (age range 11-19 years old). Whole-brain voxel-based analyses of diffusion tensor imaging metrics and region-of-interest analyses of resting-state functional connectivity (RSFC) were performed to compare patterns of brain microstructure and the corresponding RSFC between the groups. The results showed that structural disruptions of LPs and NLPs were mainly located in the occipital visual pathway. Compared with NLPs, LPs showed increased fractional anisotropy (FA) in the superior frontal gyrus and reduced diffusivity in the caudate nucleus. Moreover, the correlations between FA of the occipital cortices or mean diffusivity of the lingual gyrus and age were consistent with the development trajectory of the brain in NSCs, but inconsistent or even opposite in EBAs. Additionally, we found functional, but not structural, reorganization in NLPs compared with NSCs, suggesting that functional neuroplasticity occurs earlier than structural neuroplasticity in EBAs. Altogether, these findings provided new insights into the mechanisms underlying the neural reorganization of the brain in adolescents with early visual deprivation.

scholarly journals Predicting human resting-state functional connectivity from structural connectivity

2009 ◽  
Vol 106 (6) ◽  
pp. 2035-2040 ◽  
Author(s):  
C. J. Honey ◽  
O. Sporns ◽  
L. Cammoun ◽  
X. Gigandet ◽  
J. P. Thiran ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Resting State Functional Connectivity

scholarly journals Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian Zhang ◽  
Rosa Cortese ◽  
Nicola De Stefano ◽  
Antonio Giorgio
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Impairment ◽  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Diffusion Kurtosis Imaging ◽  
High Angular Resolution ◽  
Diffusion Spectrum Imaging ◽  
Analysis Methods ◽  
Scan Time

Cognitive impairment (CI) occurs in 43 to 70% of multiple sclerosis (MS) patients at both early and later disease stages. Cognitive domains typically involved in MS include attention, information processing speed, memory, and executive control. The growing use of advanced magnetic resonance imaging (MRI) techniques is furthering our understanding on the altered structural connectivity (SC) and functional connectivity (FC) substrates of CI in MS. Regarding SC, different diffusion tensor imaging (DTI) measures (e.g., fractional anisotropy, diffusivities) along tractography-derived white matter (WM) tracts showed relevance toward CI. Novel diffusion MRI techniques, including diffusion kurtosis imaging, diffusion spectrum imaging, high angular resolution diffusion imaging, and neurite orientation dispersion and density imaging, showed more pathological specificity compared to the traditional DTI but require longer scan time and mathematical complexities for their interpretation. As for FC, task-based functional MRI (fMRI) has been traditionally used in MS to brain mapping the neural activity during various cognitive tasks. Analysis methods of resting fMRI (seed-based, independent component analysis, graph analysis) have been applied to uncover the functional substrates of CI in MS by revealing adaptive or maladaptive mechanisms of functional reorganization. The relevance for CI in MS of SC–FC relationships, reflecting common pathogenic mechanisms in WM and gray matter, has been recently explored by novel MRI analysis methods. This review summarizes recent advances on MRI techniques of SC and FC and their potential to provide a deeper understanding of the pathological substrates of CI in MS.

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