[Show abstract][Hide abstract] ABSTRACT: Background / Purpose:
Implicit sequence learning is supported by a neural system including the striatum, medial temporal lobes (MTL), and prefrontal cortex. While the frontal regions of this network may support the attentional and motor demands of sequence learning tasks, the caudate and MTL are thought to support learning . In a recent paper we demonstrated that better implicit sequence learning performance in young adults is predicted by more positive resting state functional connectivity (rsFC) between the dorsal caudate (DC) and right MTL, suggesting that DC-MTL rsFC may reflect a person’s readiness to learn. The goal of the present study was to confirm these earlier results in a new sample and to address several limitations of our previous study.
A more positive connectivity was found between the dorsal caudate seed and clusters in the anterior cingular, left middle temporal gyrus, and right medial temporal lobe which predicted better sequence learning (SL). There were no negative correlations. The positive correlation between DC-MTL connectivity and SL is consistent with the results of our earlier study, and supports the idea that the magnitude of SL depends upon the integrity of the association between then DC and MTL at rest. The other positive correlations were unexpected given the specificity of the results of our earlier study, and may reflect several changes made to the SL task that could have promoted more interaction between explicit and implicit learning systems.
20th Annual Meeting of the Organization for Human Brain Mapping (OHBM) 2014; 07/2014
[Show abstract][Hide abstract] ABSTRACT: We examined whether modulation of functional connectivity by cognitive state differed between pre-adolescent children with Autism Spectrum Disorders (ASD) and age and IQ-matched control children. Children underwent functional magnetic resonance imaging (fMRI) during two states, a resting state followed by a sustained attention task. A voxel-wise method was used to characterize functional connectivity at two levels, local (within a voxel's 14 mm neighborhood) and distant (outside of the voxel's 14 mm neighborhood to the rest of the brain) and regions exhibiting Group × State interaction were identified for both types of connectivity maps. Distant functional connectivity of regions in the left frontal lobe (dorsolateral [BA 11, 10]; supplementary motor area extending into dorsal anterior cingulate [BA 32/8]; and premotor [BA 6, 8, 9]), right parietal lobe (paracentral lobule [BA 6]; angular gyrus [BA 39/40]), and left posterior middle temporal cortex (BA 19/39) showed a Group × State interaction such that relative to the resting state, connectivity reduced (i.e., became focal) in control children but increased (i.e., became diffuse) in ASD children during the task state. Higher state-related increase in distant connectivity of left frontal and right angular gyrus predicted worse inattention in ASD children. Two graph theory measures (global efficiency and modularity) were also sensitive to Group × State differences, with the magnitude of state-related change predicting inattention in the ASD children. Our results indicate that as ASD children transition from an unconstrained to a sustained attentional state, functional connectivity of frontal and parietal regions with the rest of the brain becomes more widespread in a manner that may be maladaptive as it was associated with attention problems in everyday life.
Frontiers in Human Neuroscience 01/2013; 7:482. · 2.91 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To study the neural networks reorganization in pediatric epilepsy, a consortium of imaging centers was established to collect functional imaging data. Common paradigms and similar acquisition parameters were used. We studied 122 children (64 control and 58 LRE patients) across five sites using EPI BOLD fMRI and an auditory description decision task. After normalization to the MNI atlas, activation maps generated by FSL were separated into three sub-groups using a distance method in the principal component analysis (PCA)-based decisional space. Three activation patterns were identified: (1) the typical distributed network expected for task in left inferior frontal gyrus (Broca's) and along left superior temporal gyrus (Wernicke's) (60 controls, 35 patients); (2) a variant left dominant pattern with greater activation in IFG, mesial left frontal lobe, and right cerebellum (three controls, 15 patients); and (3) activation in the right counterparts of the first pattern in Broca's area (one control, eight patients). Patients were over represented in Groups 2 and 3 (P < 0.0004). There were no scanner (P = 0.4) or site effects (P = 0.6). Our data-driven method for fMRI activation pattern separation is independent of a priori notions and bias inherent in region of interest and visual analyses. In addition to the anticipated atypical right dominant activation pattern, a sub-pattern was identified that involved intensity and extent differences of activation within the distributed left hemisphere language processing network. These findings suggest a different, perhaps less efficient, cognitive strategy for LRE group to perform the task.
Human Brain Mapping 05/2011; 32(5):784-99. · 6.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this study, a novel application of Principal Component Analysis (PCA) is proposed to detect language activation map patterns. These activation patterns were obtained by processing functional Magnetic Resonance Imaging (fMRI) studies on both control and localization related epilepsy (LRE) patients as they performed an auditory word definition task. Most group statistical analyses of fMRI datasets look for "commonality" under the assumption of the homogeneity of the sample. However, inter-subject variance may be expected to increase in large "normal" or otherwise heterogeneous patient groups. In such cases, certain different patterns may share a common feature, comprising of small categorical sub-groups otherwise hidden within the main pooling statistical procedure. These variant patterns may be of importance both in normal and patient groups. fMRI atypical-language patterns can be separated by qualitative visual inspection or by means of Laterality Indices (LI) based on region of interest. LI is a coefficient related to the asymmetry of distribution of activated voxels with respect to the midline and it lacks specific spatial and graphical information. We describe a mathematical and computational method for the automatic discrimination of variant spatial patterns of fMRI activation in a mixed population of control subjects and LRE patients. Unique in this study is the provision of a data-driven mechanism to automatically extract brain activation patterns from a heterogeneous population. This method will lead to automatic self-clustering of the datasets provided by different institutions often with different acquisition parameters.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:5397-400.
[Show abstract][Hide abstract] ABSTRACT: This research presents a novel application of Lateralization Index (LI) in support of a decision making process for the classification of subjects based on their brain activation patterns using functional Magnetic Resonance Imaging (fMRI) datasets. The decision process considers the subject grouping based on additional spatial information provided by the LI behavior for each individual when calculated for specific Broca's and Wernicke's language areas. The presented results were obtained applying the LI concept to assess the activation pattern on both control and Localization-related epilepsy (LRE) subjects obtained during the execution of the language network oriented paradigm referred to as the "auditory description decision task" (ADDT). Upon assessing 114 datasets, activation was observed on 103 (90%) of them, while 11 (10%) showed no activation. Among these 103 datasets, 64 (62%) datasets were presumed as control data and 39 (38%) were presumed as LRE data. The data was obtained from 5 different hospitals using the online Web-based repository site (mri-cate.fiu.edu). Masks were used for temporal and lateral brain areas for the normal brain, and individual masks were used for 48 Brodmann areas (BA). A t-test yielded a P-value of 0.0151, which indicates a statistically significant difference in the mean of both groups. The LI was also calculated using both native and normal spaces for each subject, and in this case, no statistically significant difference between the two spaces was found. It is observed that the average brain activation intensity on the LRE subjects was higher than the one observed on the control population. On contrasting the LI percentages between control and LRE data (c%, e%), the following groups were identified: a) strong right lateralization: (0%, 18%), b) right lateralization: (2%, 10%), c) bilateral: (20%, 15%), d) left lateralization: (42%, 26%), e) strong left lateralization: (36%, 31%).
23rd International Conference on Advanced Information Networking and Applications, AINA 2009, Workshops Proceedings, Bradford, United Kingdom, May 26-29, 2009; 01/2009
[Show abstract][Hide abstract] ABSTRACT: This paper describes a novel multimedia tool to facilitate visual assessment of Functional Magnetic Resonance Imaging (fMRI) activation patterns by human experts. A great effort is placed by radiologists and neurologists to present a consistent methodology to provide assessment for brain activation map images. Since each radiologist has his own way to perform the visual analysis on the images and present the findings, rating a large and heterogeneous group of images is a hard task. Although this tool is focused on assessing fMRI activation patterns related to brain language network paradigms, the tool can be extended to other brain activation maps, such as motor, reading, and working memory. Moreover, the same tool can be used for assessing images acquired using different recording modalities as long as these images are saved in standard image formats such as JPEG, BMP, or PNG. The use of this tool is independent of the methodology used to generate the brain activation map, which can be done using specialized software tools such as Statistical Parametric Mapping (SPM) or fMRI Software Library (FSL). The main benefits of using this tool for brain activation image scoring are the systematic approach for rating the activation maps, the automatic descriptive statistics applied to the results and the reduction of assessment time from several minutes to seconds. For each study, the proposed system presents the activation pattern image, based on which the rater is asked to indicate the level and type of activation observed in general, and in specific on the following areas: frontal, temporal, and supplemental motor area.
Proceedings of the Richard Tapia Celebration of Diversity in Computing Conference 2009: Intellect, Initiatives, Insight, and Innovations, Portland, Oregon, April 1-4, 2009; 01/2009
[Show abstract][Hide abstract] ABSTRACT: Purpose
This study reports a new application of the Principal Component Analysis (PCA) as a data driven decision mechanism to automatically extract brain activation patterns from a given population that is asked to perform an Auditory Description Decision Test (ADDT) paradigm with no previous knowledge of the population.
Functional Magnetic Resonance Imaging (fMRI) on 64 control and 38 epileptic subjects were processed. These datasets were acquired from 5 medical institutions. Each subject's 3D fMRI activation map was structured into 1D vector, then a 2D matrix containing the whole population was created. The PCA was applied on this 2D matrix. The position of the subject on this matrix is arbitrary, but an index table was created for tracking purposes. The PCA coefficients were fed into a distance decision making algorithm to generate the primary clusters based on the first 2 significant eigenvectors with the largest eigenvalues.
A 2D plane was used to depict the clusters on the eigenspace using only 2 eigenvectors as shown in Fig. 1. To validate the clustering technique for this specific ADDT paradigm, the mean activation for the members of the resulting clusters were calculated. Fig. 2 illustrates the mean activation pattern of each cluster with relevant activation slices for visual appreciation. Cluster 1 (67% of the population) presents high activation on the left hemisphere, which is considered typical behavior. Cluster 2 (8% of the total population and 24% of the epileptic population) exhibits a right hemisphere dominant response, which is considered atypical. Cluster 3 shows much stronger activation on the left hemisphere than cluster 1, especially in Broca's area; this is considered another typical behavior pattern. Table 1 shows the statistics of LI together with the PCA clusters. Fig. 3 plots the LI distribution based on the location of the activation.
This research shows that PCA is more effective than LI in terms of brain asymmetry activation description, since PCA reveals the actual spatial activation patterns, making evident the atypical language network behavior. LI lacks spatial and graphical information, and the use of different masks may generate totally different results.
The 30th Annual Meeting of the Society for Medical Decision Making; 10/2008
[Show abstract][Hide abstract] ABSTRACT: Autism is characterized as a spectrum of neurodevelopment impairments in communicative, social behavioural, and sensory motor skills. Public concerns about autism have grown in recent years due to the prevalence of its diagnosis in 1 out of 150 young children. Though many researches have been carried out to analyse autistic patients' EEG behaviour, an effective physiological diagnosis for autism does not exist and researchers haven't found a distinguishing pattern to classify autistic and non-autistic subjects. This preliminary study analyses the EEG data to compare patterns of speech and non- speech sound discrimination between 8 non-autistic and 4 autistic teenagers. An Artificial Neural Networks (ANNs) based classifier has been implemented to determine whether EEG data reflects differences from the two types of responses.
[Show abstract][Hide abstract] ABSTRACT: This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects' data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we present the results obtained applying the Lateralization Index (LI) concept to assess the activation pattern obtained during the execution of the language network oriented paradigm referred as "auditory description decision task" (ADDT) paradigm on control and epileptic subjects. 114 datasets were analyzed obtaining activation on 103 (90%) of them and no activation on 11 (10%); 64 (56%) datasets were used as control data and 39 (34%) as epileptic data. The data was obtained from 5 different hospitals using the online web-based repository site (mri-cate.fiu.edu). Masks were used for temporal and lateral brain areas for the normal brain, and individual masks were used for 48 Brodmann areas (BA). A t-test yielded a P-value of 0.0151, which indicates a statistically significant difference in the mean of both groups. The LI was also calculated using both native and normal spaces for each subject, and in this case, no statistically significant difference between the two spaces was found. On analyzing the LI between control and epileptic (c%, e%) data, the following groups were identified: a) strong right lateralization: (0%, 18%), b) right lateralization: (2%, 10%), c) bilateral: (20%, 15%), d) left lateralization: (42%, 26%), e) strong left lateralization: (36%, 31%).