Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

2009 ◽  
Vol 21 (5) ◽  
pp. 890-904 ◽  
Author(s):  
Janaina Mourao-Miranda ◽  
Christine Ecker ◽  
Joao R. Sato ◽  
Michael Brammer
Keyword(s):  
Pattern Recognition ◽  
Functional Connectivity ◽  
Mental Rotation ◽  
Temporal Dynamics ◽  
Parietal Lobe ◽  
Time Window ◽  
Support Vector ◽  
Dynamic Changes ◽  
Angular Disparity ◽  
Spatio Temporal

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88–99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0° vs. 20°, 0° vs. 60°, and 0° vs. 100°), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (0° vs. 100°), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100° condition in relation to the 0° condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100° condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100° condition.

scholarly journals Expansion of Eastern Redcedar (Juniperus virginiana L.) into the Deciduous Woodlands within the Forest–Prairie Ecotone of Kansas

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 154 ◽  
Author(s):  
G. A. Pabodha Galgamuwa ◽  
Jida Wang ◽  
Charles J. Barden
Keyword(s):  
Temporal Dynamics ◽  
Control Measures ◽  
Rapid Expansion ◽  
Deciduous Forests ◽  
Support Vector ◽  
Juniperus Virginiana ◽  
Eastern Redcedar ◽  
Growing Stock ◽  
Quercus Species ◽  
Spatio Temporal

North America’s midcontinent forest–prairie ecotone is currently exhibiting extensive eastern redcedar (ERC) (Juniperus virginiana L.) encroachment. Rapid expansion of ERC has major impacts on the species composition and forest structure within this region and suppresses previously dominant oak (Quercus) species. In Kansas, the growing-stock volume of ERC increased by 15,000% during 1965–2010. The overarching goal of this study was to evaluate the spatio-temporal dynamics of ERC in the forest–prairie ecotone of Kansas and understand its effects on deciduous forests. This was achieved through two specific objectives: (i) characterize an effective image classification approach to map ERC expansion, and (ii) assess ERC expansion between 1986 and 2017 in three study areas within the forest–prairie ecotone of Kansas, and especially expansion into deciduous forests. The analysis was based on satellite imagery acquired by Landsat TM and OLI sensors during 1986–2017. The use of multi-seasonal layer-stacks with a Support Vector Machine (SVM)-supervised classification was found to be the most effective approach to classify ERC distribution with high accuracy. The overall accuracies for the change maps generated for the three study areas ranged between 0.95 (95 CI: ±0.02) and 0.96 (±0.03). The total ERC cover increased in excess of 6000 acres in each study area during the 30-year period. The estimated percent increase of ERC cover was 139%, 539%, and 283% for the Tuttle Creek reservoir, Perry reservoir, and Bourbon County north study areas, respectively. This astounding rate of expansion had significant impacts on the deciduous forests where the conversion of deciduous woodlands to ERC, as a percentage of the total encroachment, were 48%, 56%, and 71%, for the Tuttle Creek reservoir, Perry reservoir, and Bourbon County north study areas, respectively. These results strongly affirm that control measures should be implemented immediately to restore the threatened deciduous woodlands of the region.

scholarly journals Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

2018 ◽  
Vol 11 (1) ◽  
pp. 37 ◽  
Author(s):  
Julien Denize ◽  
Laurence Hubert-Moy ◽  
Julie Betbeder ◽  
Samuel Corgne ◽  
Jacques Baudry ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Cover ◽  
Temporal Dynamics ◽  
Agricultural Practices ◽  
Agricultural Landscapes ◽  
Support Vector ◽  
Kappa Index ◽  
Spatio Temporal ◽  
Agricultural Areas

Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using Support Vector Machine and Random Forest algorithms in a 130 km² agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices.

scholarly journals Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

2021 ◽  
pp. 145-154
Author(s):  
Simon Dahan ◽  
Logan Z. J. Williams ◽  
Daniel Rueckert ◽  
Emma C. Robinson
Keyword(s):  
Functional Connectivity ◽  
Long Range ◽  
Temporal Dynamics ◽  
Phenotype Prediction ◽  
Spatio Temporal

scholarly journals Spatio-temporal dynamics of face perception

2019 ◽  
Author(s):  
I. Muukkonen ◽  
K. Ölander ◽  
J. Numminen ◽  
V.R. Salmela
Keyword(s):  
Temporal Dynamics ◽  
Theoretical Models ◽  
Brain Regions ◽  
Support Vector ◽  
Functional Roles ◽  
Emotional Distance ◽  
Early Visual Cortex ◽  
Spatio Temporal ◽  
Information Matrices ◽  
Combine Information

AbstractThe temporal and spatial neural processing of faces have been studied rigorously, but few studies have unified these dimensions to reveal the spatio-temporal dynamics postulated by the models of face processing. We used support vector machine decoding and representational similarity analysis to combine information from different locations (fMRI), timepoints (EEG), and theoretical models. By correlating information matrices derived from pair-wise decodings of neural responses to different facial expressions (neutral, happy, fearful, angry), we found early EEG timepoints (110-150 ms) to match fMRI data from early visual cortex (EVC), and later timepoints (170 – 250 ms) to match data from occipital and fusiform face areas (OFA/FFA) and posterior superior temporal sulcus (pSTS). The earliest correlations were driven by information from happy faces, and the later by more accurate decoding of fearful and angry faces. Model comparisons revealed systematic changes along the processing hierarchy, from emotional distance and visual feature coding in EVC to coding of intensity of expressions in right pSTS. The results highlight the importance of multimodal approach for understanding functional roles of different brain regions.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

2021 ◽  
Author(s):  
Yue Cheng ◽  
Gaoyan Zhang ◽  
Xiaodong Zhang ◽  
Yuexuan Li ◽  
Jingli Li ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Functional Connectivity ◽  
Time Window ◽  
Brain Connectivity ◽  
Minimal Hepatic Encephalopathy ◽  
Classification Performance ◽  
Support Vector ◽  
Dynamic Features ◽  
Dynamic Functional Connectivity ◽  
Window Approach

Abstract To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n=30; noHE, n=32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary , DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

scholarly journals Exploring concomitant neuroimaging and genetic alterations in patients with and patients without auditory verbal hallucinations: A pilot study and mini review

2020 ◽  
Vol 48 (7) ◽  
pp. 030006051988485
Author(s):  
Haiping Yu ◽  
Wang Ying ◽  
Gang Li ◽  
Xiaodong Lin ◽  
Deguo Jiang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Pilot Study ◽  
Functional Connectivity ◽  
Diagnostic Accuracy ◽  
High Throughput Sequencing ◽  
Parietal Lobe ◽  
Genetic Alterations ◽  
Functional Connectivity Density ◽  
Auditory Verbal Hallucinations ◽  
T Values

Objective To explore concomitant neuroimaging and genetic alterations in patients with schizophrenia with or without auditory verbal hallucinations (AVHs), and to discuss the use of pattern recognition techniques in the development of an objective index that may improve diagnostic accuracy and treatment outcomes for schizophrenia. Methods The pilot study included patients with schizophrenia with AVHs (SCH-AVH group) and without AVHs (SCH-no AVH group). High throughput sequencing (HTS) was performed to explore RNA networks. Global functional connectivity density (gFCD) analysis was performed to assess functional connectivity (FC) alterations of the default mode network (DMN). Quantitative long noncoding (lnc) RNA and mRNA expression data were related to peak T values of gFCDs using Pearson’s correlation coefficient analysis. Results Compared with the SCH-no AVH group ( n = 5), patients in the SCH-AVH group ( n = 5) exhibited differences in RNA expression in RNA networks that were related to AVH severity, and displayed alterations in FC (reflected by gFCD differences) within the DMN (posterior cingulate and dorsal-medial prefrontal cortex), and in the right parietal lobe, left occipital lobe, and left temporal lobe. Peak lncRNA expression values were significantly related to peak gFCD T values within the DMN. Conclusion Among patients with schizophrenia, there are concomitant FC and genetic expression alterations associated with AVHs. Data from pattern recognition studies may inform the development of an objective index aimed at improving early diagnostic accuracy and treatment planning for patients with schizophrenia with and without AVHs.

scholarly journals Altered Homotopic Functional Connectivity Within White Matter in the Early Stages of Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Pan Wang ◽  
Zedong Wang ◽  
Jianlin Wang ◽  
Yuan Jiang ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
White Matter ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Parietal Lobe ◽  
Neurodegenerative Disorder ◽  
Magnetic Resonance Images ◽  
Support Vector

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with memory loss and cognitive impairment. The white matter (WM) BOLD signal has recently been shown to provide an important role in understanding the intrinsic cerebral activity. Although the altered homotopic functional connectivity within gray matter (GM-HFC) has been examined in AD, the abnormal HFC to WM remains unknown. The present study sought to identify changes in the WM-HFC and anatomic characteristics by combining functional magnetic resonance imaging with diffusion tensor imaging (DTI). Resting-state and DTI magnetic resonance images were collected from the OASIS-3 dataset and consisted of 53 mild cognitive impairment (MCI) patients, 90 very MCI (VMCI), and 100 normal cognitive (NC) subjects. Voxel-mirrored HFC was adopted to examine whether WM-HFC was disrupted in VMCI and MCI participants. Moreover, the DTI technique was used to investigate whether specific alterations of WM-HFC were associated with anatomic characteristics. Support vector machine analyses were used to identify the MCI and VMCI participants using the abnormal WM-HFC as the features. Compared with NC, MCI, and VMCI participants showed significantly decreased GM-HFC in the middle occipital gyrus and inferior parietal gyrus and decreased WM-HFC in the bilateral middle occipital and parietal lobe-WM. In addition, specific WM-functional network alteration for the bilateral sub-lobar-WM was found in MCI subjects. MCI subjects showed abnormal anatomic characteristics for bilateral sub-lobar and parietal lobe-WM. Results of GM-HFC mainly showed common neuroimaging features for VMCI and MCI subjects, whereas analysis of WM-HFC showed specific clinical neuromarkers and effectively compensated for the lack of GM-HFC to distinguish NC, VMCI, and MCI subjects.

scholarly journals Distinct roles for the Anterior Temporal Lobe and Angular Gyrus in the spatio-temporal cortical semantic network

2019 ◽  
Author(s):  
Seyedeh-Rezvan Farahibozorg ◽  
Richard N. Henson ◽  
Anna M. Woollams ◽  
Olaf Hauk
Keyword(s):  
Functional Connectivity ◽  
Temporal Lobe ◽  
Time Window ◽  
Inferior Frontal Gyrus ◽  
Semantic Knowledge ◽  
Angular Gyrus ◽  
Anterior Temporal Lobe ◽  
Sensory Motor ◽  
Semantic Variables ◽  
Spatio Temporal

AbstractIt is now well recognised that human semantic knowledge is supported by a large neural network distributed over multiple brain regions, but the dynamic organisation of this network remains unknown. Some studies have proposed that a central semantic hub coordinates this network. We explored the possibility of different types of semantic hubs; namely “representational hubs”, whose neural activity is modulated by semantic variables, and “connectivity hubs”, whose connectivity to distributed areas is modulated by semantic variables. We utilised the spatio-temporal resolution of source-estimated Electro-/Magnetoencephalography data in a word-concreteness task (17 participants, 12 female) in order to: (i) find representational hubs at different timepoints based on semantic modulation of evoked brain activity in source space; (ii) identify connectivity hubs among left Anterior Temporal Lobe (ATL), Angular Gyrus (AG), Middle Temporal Gyrus and Inferior Frontal Gyrus based on their functional connectivity to the whole cortex, in particular sensory-motor-limbic systems; and (iii) explicitly compare network models with and without an intermediate hub linking sensory input to other candidate hub regions using Dynamic Causal Modelling (DCM) of evoked responses. ATL’s activity was modulated as early as 150ms post-stimulus, while both ATL and AG showed modulations of functional connectivity with sensory-motor-limbic areas from 150-450ms. DCM favoured models with one intermediate hub, namely ATL in an early time window and AG in a later time-window. Our results support ATL as a single representational hub with an early onset, but suggest that both ATL and AG function as connectivity hubs depending on the stage of semantic processing.

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