scholarly journals Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke

2020 ◽  
Author(s):  
Mohit H Adhikari ◽  
Joseph Griffis ◽  
Joshua S. Siegel ◽  
Michel Thiebaut de Schotten ◽  
Gustavo Deco ◽  
...  
Keyword(s):  
Resting State ◽  
Early Stage ◽  
Effective Connectivity ◽  
Individual Subject ◽  
Whole Brain ◽  
Long Term Outcome ◽  
Clinical Prognosis ◽  
State Activity ◽  
One Year ◽  
Time Point

ABSTRACTRecent resting-state fMRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity (FC) between homotopic regions of the same network, and an abnormal increase of ipsilesional FC between task-negative and task-positive resting-state networks (RSNs). Whole-brain computational modeling studies, at the individual subject level, using undirected effective connectivity (EC) derived from empirically measured FC, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional EC from zero-lagged and lagged FC, then, to compare it to empirically measured FC for predicting stroke vs. healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of FC vs. model EC in predicting the long-term outcome from acute measures.Both FC and EC predicted healthy from stroke individuals significantly better than the chance-level, however, EC accuracy was significantly higher than that of FC at 1-2 weeks, three months, and one year post-stroke. The predictive FC links mainly included those reported in previous studies (within-network inter-hemispheric, and between task-positive and -negative networks intra-hemispherically). Predictive EC links included additional between-network links. EC was a better predictor than FC of the number of behavioral domains in which patients suffered deficits, both at two weeks and one-year post onset of stroke. Interestingly, patient deficits at one-year time point were better predicted by EC values at two weeks rather than at one-year time point. Our results thus demonstrate that the second-order statistics of fMRI resting-state activity at an early stage of stroke, derived from a whole-brain EC, estimated in a model fitted to reproduce the propagation of BOLD activity, has pertinent information for clinical prognosis.

scholarly journals Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

NeuroImage ◽  
2020 ◽  
Vol 208 ◽  
pp. 116367 ◽  
Author(s):  
Giulia Prando ◽  
Mattia Zorzi ◽  
Alessandra Bertoldo ◽  
Maurizio Corbetta ◽  
Marco Zorzi ◽  
...  
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Whole Brain

Serum VEGF in patients (PTS) receiving bevacizimab (BEV) for early-stage breast cancer (ESBC): ICORG 08-10.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e13520-e13520
Author(s):  
Norma O'Donovan ◽  
Alexandra Canonici ◽  
Imelda Parker ◽  
Tanya O'Shea ◽  
Brian Moulton ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Enzyme Linked Immunosorbent Assay ◽  
Vascular Endothelial ◽  
Serum Samples ◽  
Median Level ◽  
Her 2 ◽  
One Year ◽  
Endothelial Growth Factor ◽  
Time Point

e13520 Background: Approximately 30% of pts with ESBC develop metastasis despite apparently curative loco-regional therapy. Angiogenesis, partly mediated by vascular endothelial growth factor (VEGF), may promote metastases. BEV, an anti-VEGF monoclonal antibody which has some efficacy in metastatic BC is being studied as an adjuvant in ESBC, but there are no validated biomarkers, and the effect on VEGF levels in pts with ESBC is unknown. We studied VEGF serum concentration in ESBC receiving BEV. Methods: 106 pts with HER-2 negative ESBC were included in this study. Patients received 4 cycles of docetaxel (75 mg/m2) + cyclophosphamide (600 mg/m2) with BEV (15 mg/kg) once every three weeks for one year. Serum samples were collected prior to commencement of treatment, at 6 months and 12 months. The VEGF levels in serum samples were determined, at each time point, using a VEGF enzyme-linked immunosorbent assay (ELISA). Results: VEGF concentration was determined in serum samples from 65 patients. Three pts (5%) had no detectable VEGF at 0, 6 and 12 months. The median level of serum VEGF in the remaining 62 patients was 325.4 ± 244 pg/ml. These 62 patients showed a significant decrease in VEGF concentration after 6 and 12 months of treatment (median 9 ± 42.1 pg/ml, p<0.001 and 9 ± 43.9 pg/ml, p<0.001 respectively). However, no significant change in VEGF levels were observed at 12 months compared to 6 months (median 0 ± 60.3 pg/ml, p=0.704). Conclusions: Adjuvant therapy with chemotherapy and BEV is associated with a significant reduction in VEGF levels at 6 months.

Aberrant Functional and Effective Connectivity of the Frontostriatal Network in Unilateral Acute Tinnitus Patients with Hearing Loss

2021 ◽  
Author(s):  
Gang-Ping Zhou ◽  
Yu-Chen Chen ◽  
Wang-Wei Li ◽  
Heng-Le Wei ◽  
Yu-Sheng Yu ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Resting State ◽  
Large Scale ◽  
Early Stage ◽  
Effective Connectivity ◽  
Causality Analysis ◽  
Resting State Functional Connectivity ◽  
Midcingulate Cortex ◽  
Tinnitus Distress ◽  
Acute Tinnitus

Abstract Purpose: The present study combined resting-state functional connectivity (FC) and Granger causality analysis (GCA) to explore frontostriatal network dysfunction in unilateral acute tinnitus (AT) patients with hearing loss. Methods: The participants included 42 AT patients and 43 healthy control (HC) subjects who underwent resting-state functional magnetic resonance imaging (fMRI) scans. Based on the seed regions in the frontostriatal network, FC and GCA were conducted between the AT patients and HC subjects. Correlation analyses were used to examine correlations among altered FC values, GCA values, and clinical features in AT patients. Results: Compared with HCs, AT patients showed a general reduction in FC between the seed regions in the frontostriatal network and nonauditory areas, including the frontal cortices, midcingulate cortex (MCC) , supramarginal gyrus (SMG), and postcentral gyrus (PoCG) . Using the GCA algorithm, we detected abnormal effective connectivity (EC) in the inferior occipital gyrus (IOG), MCC, Cerebelum_Crus1, and PoCG. Furthermore, correlations between disrupted FC/EC and clinical characteristics, especially tinnitus distress-related characteristics, were found in AT patients. Conclusions: Our work demonstrated abnormal FC and EC between the frontostriatal network and several nonauditory regions in AT patients with hearing loss, suggesting that multiple large-scale network dysfunctions and interactions are involved in the perception of tinnitus. These findings not only enhance the current understanding of the frontostriatal network in tinnitus but also serve as a reminder of the importance of focusing on tinnitus at an early stage.

scholarly journals Regression dynamic causal modeling for resting-state fMRI

2020 ◽  
Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Computationally Efficient ◽  
Dynamic Causal Modeling ◽  
Whole Brain ◽  
Wide Range

Abstract“Resting-state” functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks.Here, we show that a method recently developed for task-fMRI – regression dynamic causal modeling (rDCM) – extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

scholarly journals Decreased integration and information capacity in stroke measured by whole brain models of resting state activity

Brain ◽  
2017 ◽  
Vol 140 (4) ◽  
pp. 1068-1085 ◽  
Author(s):  
Mohit H. Adhikari ◽  
Carl D. Hacker ◽  
Josh S. Siegel ◽  
Alessandra Griffa ◽  
Patric Hagmann ◽  
...  
Keyword(s):  
Resting State ◽  
Information Capacity ◽  
Whole Brain ◽  
State Activity ◽  
Brain Models

scholarly journals Reliable local dynamics in the brain across sessions are revealed by whole‐brain modeling of resting state activity

2019 ◽  
Author(s):  
Patricio Donnelly‐Kehoe ◽  
Victor M. Saenger ◽  
Nina Lisofsky ◽  
Simone Kühn ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Resting State ◽  
Whole Brain ◽  
Local Dynamics ◽  
State Activity ◽  
Brain Modeling ◽  
The Brain

Resting-state brain alteration after a single dose of SSRI administration predicts 8-week remission of patients with major depressive disorder

2016 ◽  
Vol 47 (3) ◽  
pp. 438-450 ◽  
Author(s):  
Y. Cheng ◽  
J. Xu ◽  
D. Arnone ◽  
B. Nie ◽  
H. Yu ◽  
...  
Keyword(s):  
Resting State ◽  
Pharmacological Treatment ◽  
Early Stage ◽  
Antidepressant Treatment ◽  
Occipital Cortex ◽  
First Episode ◽  
Acute Administration ◽  
Functional Magnetic Resonance ◽  
Major Depressive ◽  
State Activity

BackgroundThe present study investigated alteration of brain resting-state activity induced by antidepressant treatment and attempted to investigate whether treatment efficacy can be predicted at an early stage of pharmacological treatment.MethodForty-eight first-episode medication-free patients diagnosed with major depression received treatment with escitalopram. Resting-state functional magnetic resonance imaging was administered prior to treatment, 5 h after the first dose, during the course of pharmacological treatment (week 4) and at endpoint (week 8). Resting-state activity was evaluated in the course of the 8-week treatment and in relation to clinical improvement.ResultsEscitalopram dynamically modified resting-state activity in depression during the treatment. After 5 h the antidepressant induced a significant decrease in the signal in the occipital cortex and an increase in the dorsolateral and dorsomedial prefrontal cortices and middle cingulate cortex. Furthermore, while remitters demonstrated more obvious changes following treatment, these were more modest in non-responders suggesting possible tonic and dynamic differences in the serotonergic system. Changes after 5 h in the caudate, occipital and temporal cortices were the best predictor of clinical remission at endpoint.ConclusionsThis study revealed the possibility of using the measurement of resting-state neural changes a few hours after acute administration of antidepressant to identify individuals likely to remit after a few weeks of treatment.

scholarly journals Anatomically Standardized Detection of MRI Atrophy Patterns in Early-Stage Alzheimer’s Disease

2021 ◽  
Vol 11 (11) ◽  
pp. 1491
Author(s):  
Lukas Lenhart ◽  
Stephan Seiler ◽  
Lukas Pirpamer ◽  
Georg Goebel ◽  
Thomas Potrusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Verbal Memory ◽  
Early Stage ◽  
Disease Onset ◽  
Principal Component ◽  
Clinical Dementia Rating ◽  
Individual Subject ◽  
Whole Brain

MRI studies have consistently identified atrophy patterns in Alzheimer’s disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.

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