Significant Regions Of Interest Research Articles

Early‐life bilingualism could protect from age‐related gray matter volume loss in the left caudate – preliminary results

AbstractBackgroundEarly‐life bilingualism (i.e., second language acquisition before the age of 30) has been shown to confer benefits in separate cognitive domains later in life, possibly contributing to the concept of cognitive reserve. However, the effects of early‐life bilingualism on cognitive performance and gray matter volume (GMV) in the elderly have been studied scarcely. In an ongoing cohort, we examined the influence of early‐life bilingualism on cognitive performance (e.g., phonemic fluency as tested by the Regensburg word fluency test (RWT), among others) as well as age‐related brain gray matter volume in older adults.MethodWe analysed 3‐Tesla T1‐weighted Multi‐Echo MPRAGE and cognitive data from 25 healthy adults (72.8±7.69 years, 10 female) in our new cohort established within the CRC1436 (https://sfb1436.de/). Assessment of bilingualism was conducted using the Lifetime of Experiences Questionnaire (LEQ). MRI data were preprocessed and analysed using FSL‐VBM. We performed a whole‐brain analysis looking for an interaction between age and bilingualism on GMV values, postulating that early bilingualism might contribute to a preservation of gray matter along the age trajectory. We extracted GMV values from significant regions of interest (ROIs) and looked for correlations with cognitive performance. Due to the small cohort and the preliminary nature of these analyses, we chose an exploratory threshold of p<0.001 (uncorrected) for the whole brain analysis. ROIs were defined by a threshold of p<0.1 cluster‐corrected (z>3.1).ResultParticipants with early‐life bilingualism (n = 7) had significantly more years of education (p<0.05) and performed better in the RWT (p<0.05) than monolinguals (n = 18). The age‐bilingualism interaction term across the whole brain (adjusted for education) identified the head of left caudate nucleus as being protected from age‐related GMV loss by early‐life bilingualism (Fig. 1). The age‐GMV‐relationship for both groups is shown in Fig. 2. We observed no significant relationship between GMV in the left caudate and performance in any cognitive tests.ConclusionThese preliminary results hint at a role of early‐life bilingualism in modulating GMV in older adults. How these relate to better cognitive performance remains to be elucidated. Moreover, considering brain areas not typically studied in cognitive aging, such as the basal ganglia, seems worthwhile.

Risk architecture for altered hippocampal connectivity in normal aging differ between men and women

AbstractBackgroundFunctional MRI (fMRI) studies have associated impaired memory networks with increase in AD pathology. Recently, cross‐sectional analyses found b‐amyloid (Aβ) deposition associated with different patterns of hippocampal connectivity in a memory task based on sex and white matter hyperintensity volume (WMHV). However, cross‐sectional studies don’t provide insight into the driving mechanisms of network change. In this longitudinal study, we identify risk factors based on sex that are associated with memory network connectivity changes.MethodOur study included 54 cognitively normal older adults (N = 34 female, mean age 74.6 years) with MRI scans on average 2.43 (SD 0.82) years apart. T1w, T2‐FLAIR MRI, and PiB PET were collected to quantify cortical volume, WMHV and Aβ deposition, respectively. Task fMRI was collected while participants performed face‐name associative memory tasks. Left and right hippocampus seeds were used to estimate functional connectivity between hippocampus and voxels in the brain. Second‐level analyses were performed to determine regions of interest (ROI) with significant differences in sex by time interaction (p<0.001). The mean beta values were extracted from the significant ROIs for post‐hoc analysis where we performed multivariate linear regression analyses to find correlations with connectivity change (follow‐up minus baseline).ResultThree ROIs presented significant sex by time interactions: left‐right hippocampus, hippocampus‐anterior cingulate cortex (ACC), and hippocampus‐medial frontal gyrus. The change in left‐right hippocampus connectivity increased for women and decreased for men (Fig.1). For the other ROIs, connectivity increased for men and decreased for women (Fig.2). Left‐right hippocampal connectivity change had a significant sex‐WMHV interaction effect (p<0.01), where only women had a positive association between connectivity change and baseline WMHV. Hippocampus‐ACC connectivity change had a significant sex‐Aβ interaction (p<0.01), with greater baseline Aβ associated with increased connectivity change for men and decreased for women.ConclusionWe observed sex‐differences in risk architecture for altered hippocampal connectivity. In the presence of vascular pathology and Aβ, women appear to experience local hippocampal connectivity and decrease hippocampal‐frontal connectivity. Meanwhile, men experienced hippocampal‐frontal connectivity with increased Aβ burden. Characterizing these sex differences in memory network alterations can help guide the development of sex‐specific prevention and treatment strategies.

Characterization of GEXP15 as a Potential Regulator of Protein Phosphatase 1 in Plasmodium falciparum.

The Protein Phosphatase type 1 catalytic subunit (PP1c) (PF3D7_1414400) operates in combination with various regulatory proteins to specifically direct and control its phosphatase activity. However, there is little information about this phosphatase and its regulators in the human malaria parasite, Plasmodium falciparum. To address this knowledge gap, we conducted a comprehensive investigation into the structural and functional characteristics of a conserved Plasmodium-specific regulator called Gametocyte EXported Protein 15, GEXP15 (PF3D7_1031600). Through in silico analysis, we identified three significant regions of interest in GEXP15: an N-terminal region housing a PP1-interacting RVxF motif, a conserved domain whose function is unknown, and a GYF-like domain that potentially facilitates specific protein-protein interactions. To further elucidate the role of GEXP15, we conducted in vitro interaction studies that demonstrated a direct interaction between GEXP15 and PP1 via the RVxF-binding motif. This interaction was found to enhance the phosphatase activity of PP1. Additionally, utilizing a transgenic GEXP15-tagged line and live microscopy, we observed high expression of GEXP15 in late asexual stages of the parasite, with localization predominantly in the nucleus. Immunoprecipitation assays followed by mass spectrometry analyses revealed the interaction of GEXP15 with ribosomal- and RNA-binding proteins. Furthermore, through pull-down analyses of recombinant functional domains of His-tagged GEXP15, we confirmed its binding to the ribosomal complex via the GYF domain. Collectively, our study sheds light on the PfGEXP15-PP1-ribosome interaction, which plays a crucial role in protein translation. These findings suggest that PfGEXP15 could serve as a potential target for the development of malaria drugs.

Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants.

Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.

ODMNet: Automated Glaucoma Detection and Classification Model Using Heuristically-Aided Optimized DenseNet and MobileNet Transfer Learning

In various existing works, glaucoma detection is not predicted accurately, which may lead to irreversible vision loss. A new framework is designed for detecting glaucoma by transfer learning approach. In the initial stage, the source images are gathered from standard datasets. After collecting the raw images, it is fed for image enhancement, performed through the Retinex approach. Further, the segmentation process is carried out by Modified DeepLabV3, where the significant Regions of Interest (ROI) are extracted by enhanced images and segmented the abnormalities. To meet the optimal value, the parameters in DeepLabV3 are tuned optimally by the Improved Rain Optimization Algorithm (IROA). Once the image is segmented, it is subjected to the detection or classification task, where the glaucoma is effectively classified by the hybrid learning method called Optimized DenseNet and MobileNet Transfer Learning (ODMNet) that is constructed with Densely Connected Convolutional Networks (DenseNet) and MobileNet, where the layers are optimized by IROA approach. Finally, the performance is assessed with the assistance of diverse metrics. In experimental analysis, the accuracy and F1-score of the designed method attain 96% and 93%. The recommended detection model achieves higher detection performance in telemedicine and healthcare applications.

Tracking the Progression and Influence of Beta-Amyloid Plaques Using Percolation Centrality and Collective Influence Algorithm: A Study Using PET Images.

The study of brain networks, particularly the spread of disease, is made easier thanks to the network theory. The aberrant accumulation of beta-amyloid plaques and tau protein tangles in Alzheimer's disease causes disruption in brain networks. The evaluation scores, such as the mini-mental state examination (MMSE) and neuropsychiatric inventory questionnaire, which provide a clinical diagnosis, are affected by this build-up. The percolation of beta-amyloid/tau tangles and their impact on cognitive tests are still unspecified. Percolation centrality could be used to investigate beta-amyloid migration as a characteristic of positron emission tomography (PET)-image-based networks. The PET-image-based network was built utilizing a public database containing 551 scans published by the Alzheimer's Disease Neuroimaging Initiative. Each image in the Julich atlas has 121 zones of interest, which are network nodes. Furthermore, the influential nodes for each scan are computed using the collective influence algorithm. For five nodal metrics, analysis of variance (ANOVA; P < .05) reveals the region of interest (ROI) in gray matter (GM) Broca's area for Pittsburgh compound B (PiB) tracer type. The GM hippocampus area is significant for three nodal metrics in the case of florbetapir (AV45). Pairwise variance analysis of the clinical groups reveals five to twelve statistically significant ROIs for AV45 and PiB, respectively, that can distinguish between pairs of clinical situations. Based on multivariate linear regression, the MMSE is a trustworthy evaluation tool. Percolation values suggest that around 50 of the memory, visual-spatial skills, and language ROIs are critical to the percolation of beta-amyloids within the brain network when compared to the other extensively used nodal metrics. The anatomical areas rank higher with the advancement of the disease, according to the collective influence algorithm.

Neurostructural correlates of retinal microvascular caliber in adolescent bipolar disorder.

Vascular-brain associations are well established in adults but neglected in youth and psychiatric populations, who are at greater cardiovascular risk. We therefore examined the association of retinal vascular caliber with regional brain structure in adolescents with and without bipolar disorder (BD). One hundred and three adolescents (n=51 BD, n=52 healthy control [HC]) completed retinal fundus imaging, yielding arteriolar and venular diameters, followed by T1-weighted 3-Tesla MRI. Region of interest (ROI) analyses examined ventrolateral prefrontal cortex (vlPFC) and ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), amygdala, and hippocampus, complemented by vertex-wise analyses. Linear regression assessed the association between retinal measures and brain structure, adjusting for covariates including age, sex, BMI, and intracranial volume (ICV). In the overall sample, arteriolar caliber was negatively associated with ACC volume (β=-0.20, p uncorrected =.046) and surface area (β=-0.19, p uncorrected =.049). There were no other significant ROI findings. Vertex-wise analyses detected several significant positive bilateral associations of arteriovenous ratio (AVR) with volume and surface area in regions including rostral middle frontal gyrus (left p=.001; right p=.006), isthmus cingulate cortex (left and right p<.001), and left precuneus (p<.001). Significant negative associations were also observed for AVR (p=.03) and arteriolar caliber (p=.01), including a cluster encompassing the left rostral middle frontal gyrus and orbitofrontal cortical thickness. In the sole retinal-by-diagnosis interaction, greater AVR was more strongly associated with lower volume in the left middle temporal and fusiform gyri in BD versus HC (p=.004). This study provides evidence that vascular-brain associations are already evident in adolescence, suggesting that optimizing cardiovascular health may benefit the brain. This may be particularly relevant in BD and other brain disorders. Future research focusing on subpopulations where vascular-brain associations may be especially strong, for whom vascular-related interventions may be most indicated, is warranted.

Dissection of neuroinflammation in schizophrenia

AimsSchizophrenia is notoriously becoming one of the world's most debilitating mental disorders, affecting 1 in 100 people. There is increasing evidence that neuroinflammation plays a part in the pathogenesis of schizophrenia and other psychotic disorders; microglial activity acting as a marker for neuroinflammatory reactions in the brain. Furthermore, cannabis is an illicit substance that also evokes a similar response in the neuroimmune activity. This project explores how cannabis exposure influences an elevation in neuroinflammatory responses through TSPO levels, and whether this information can help us determine if cannabis use and increased TSPO levels can be associated with a risk factor for developing psychosis.Method55 participants (36 males and 19 females) were recruited from the community by the IRIS (Inflammatory Reaction in Schizophrenia) team at the IoPPN, King's College London, from which 34 patients with a diagnosis of schizophrenia and 21 healthy controls took part in the study. The eligible participants underwent clinical assessments and PET scanning, from which cannabis use history and PET data were collected. Participant neuroinflammatory levels are represented by [18F]DPA-714 volume and different regions of grey matter in the brain were analysed through multivariate analyses, the confounding variables being age and TSPO genotype.ResultA statistically significant association is shown between participants who have had exposure to cannabis and participants who have not had any exposure in their lifetime. The differences across the prioritised brain regions of interest were robust, the association appearing more apparent and statistically significant in the total (p = .00) and temporal grey matter (p = .00) regions of the brain. This may suggest that cannabis exposure influences the [18F]DPA-714 VT in the significant regions of interest. However, a negative association is seen with current use, the quantity of use, and the frequency of use.ConclusionThe initial findings for cannabis exposure show us a positive association with increased TSPO levels, however, limitations must be taken into account. Although we cannot readily establish that elevated TSPO levels in cannabis users can presently act as a risk factor marker for developing psychosis from this particular study, we can utilise this data to continue our research in disclosing a new system to predict the occurrence of psychosis.

Brain response to emotional faces in anxiety and depression: neural predictors of cognitive behavioral therapy outcome and predictor-based subgroups following therapy.

Neuroimaging studies have shown variance in brain response to emotional faces predicts cognitive behavioral therapy (CBT) outcome. An important next step is to determine if individual differences in neural predictors of CBT response represent distinct patient groups. In total, 90 patients with internalizing disorders completed a face-matching task during functional magnetic resonance imaging before and after 12 weeks of CBT and 45 healthy controls completed the task before and after 12 weeks. Patients exhibiting a pre-to-post CBT >50% reduction in symptom severity on two measures were considered treatment responders. Regions of interest (ROIs) for angry, fearful, and happy faces were submitted to receiver operating characteristic (ROC) curve analysis. Significant ROIs were then submitted to decision tree analysis to classify responder/non-responder subgroups. Psychophysiological interactions (PPI) were used to explore functional connectivity in the region(s) that delineated subgroups. A total of 51 patients were treatment responders and ROC curve results were significant for all face types though specific regions varied. Decision tree results revealed superior occipital response to angry faces identified patient subgroups such that the subgroup with 'high' occipital activity had more responders than the 'low' occipital subgroup. Following CBT, the high, relative to low, occipital subgroup was less symptomatic. Controls exhibited stable superior occipital activation over time. Whole-brain PPI showed reduced baseline superior occipital-postcentral gyrus functional connectivity in responders compared to non-responders. Preliminary findings indicate patients characterized by relatively more pre-treatment superior occipital gyrus engagement to angry faces and reduced superior occipital-postcentral gyrus connectivity, relative to non-responders, may represent a phenotype likely to benefit from CBT.

Beery VMI and Brain Volumetric Relations in Autism Spectrum Disorder.

Although diminished proficiency on tasks that require visual-motor integration (VMI) has been reported in individuals with autism spectrum disorder (ASD), very few studies have examined the association between VMI performance and neuroanatomical regions of interest (ROI) involved in motor and perceptual functioning. To address these issues, the current study included an all-male sample of 41 ASD (ages 3-23 years) and 27 typically developing (TD) participants (ages 5-26 years) who completed the Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery VMI) as part of a comprehensive neuropsychological battery. All participants underwent 3.0 T magnetic resonance imaging (MRI) with image quantification (FreeSurfer software v5.3). The groups were statistically matched on age, handedness, and intracranial volume (ICV). ASD participants performed significantly lower on VMI and IQ measures compared with the TD group. VMI performance was significantly correlated with FSIQ and PIQ in the TD group only. No pre-defined neuroanatomical ROIs were significantly different between groups. Significant correlations were observed in the TD group between VMI and total precentral gyrus gray matter volume (r = .51, p = .006) and total frontal lobe gray matter volume (r = .46, p = .017). There were no significant ROI correlations with Beery VMI performance in ASD participants. At the group level, despite ASD participants exhibiting reduced visuomotor abilities, no systematic relation with motor or sensory-perceptual ROIs was observed. In the TD group, results were consistent with the putative role of the precentral gyrus in motor control along with frontal involvement in planning, organization, and execution monitoring, all essential for VMI performance. Given that similar associations between VMI and ROIs were not observed in those with ASD, neurodevelopment in ASD group participants may not follow homogenous patterns making correlations in these brain regions unlikely to be observed.

Alterations in knee sensorimotor brain functional connectivity contributes to ACL injury in male high-school football players: a prospective neuroimaging analysis

ObjectiveThis study’s purpose was to utilize a prospective dataset to examine differences in functional brain connectivity in male high school athletes who suffered an anterior cruciate ligament (ACL) injury relative to their non-injured peers. MethodsSixty-two male high school football players were evaluated using functional magnetic resonance imaging prior to their competitive season to evaluate resting-state functional brain connectivity. Three athletes later experienced an ACL injury and were matched to 12 teammates who did not go on to sustain an ACL injury (controls) based on school, age, height, weight, and year in school. Twenty-five knee-motor regions of interest (ROIs) were created to identify differences in connectivity between the two groups. Between-subject F and t tests were used to identify significant ROI differences using a false discovery rate correction for multiple comparisons. ResultsThere was significantly less connectivity between the left secondary somatosensory cortex and the left supplementary motor area (p = 0.025), right pre-motor cortex (p = 0.026), right supplementary motor area (p = 0.026), left primary somatosensory cortex (superior division; p = 0.026), left primary somatosensory cortex (inferior division; p = 0.026), and left primary motor cortex (p = 0.048) for the ACL-injured compared to the control subjects. No other ROI-to-ROI comparisons were significantly different between the groups (all p > 0.05). ConclusionOur preliminary data indicate a potential sensorimotor disruption for male football players who go on to experience an ACL injury. Future studies with larger sample sizes and complementary measures of neuromuscular control are needed to support these findings.

Automated approaches for ROIs extraction in medical thermography: a review and future directions

Historically, the skin temperature has been used as a functional indicator of the processes undergoing inside the body, where the anomalous change in temperature gives indication of illness. Infrared thermography (IRT) is a non-invasive approach which radiometrically measures the temperature distribution on the object’s surface based on emanating IR radiation. In medical field, IRT has been used to diagnose the various problems associated with superficial body parts since 1960s. The clinical abnormalities can be diagnosed by visual and subjective analysis of the thermograms, but human based diagnoses are likely to be influenced by narcissus effect, negligence, visual exhaustion and mental workload. Apart, the deep and objective diagnostic information, like severity and type of a disease can only be obtained by statistically analyse the region of interest (ROI). So, the segmentation of ROI has often been an initial step in medical diagnosis, where the automated approaches lead to fast and highly reproducible analysis. Recent advancement in deep learning opens a new way for effective computer-aided medical diagnosis, several studies have proposed deep learning based automatic segmentation and data classification. This review focuses on the wide range of image processing techniques adopted for automatic segmentation of various clinically significant ROIs, like breast, face, foot, hand etc. In addition, the present efforts are targeted on the selection of characteristic points for an effective automatic segmentation and identification of new areas to carry out the further research. Besides, the basics of medical IRT, need of automatic segmentation and factors which hinder the automatic segmentation in IRT are also discussed.

Altered resting-state functional connectivity in depressive disorder patients with suicidal attempts

In this study, we attempted to examine the resting state functional connectivity (resting state FC) and its correlation with clinical features in depressed disorder patients with suicidal attempts (SA). A total of 32 individuals participated in this study: 12 depressive disorder patients with SA and 20 healthy controls were included. Resting state FC was investigated between the two groups by region of interest (ROI) to ROI analysis. Then seed to voxel analysis was performed using significant ROIs as a seed. The correlations of significant resting state FC with clinical data were analyzed. The connectivity of anterior division of right parahippocampus-posterior division of left parahippocampus (pPaHCl) and temporooccipital part of right inferior temporal gyrus-right frontal eye field of dorsal attention network (FEFrDAN) were significantly increased, while connectivity of medial frontal cortex (MedFC)-right supplementary motor cortex (SMAr) was significantly decreased in the patients (p-FDR [false discovery rate] <0.05). The patient group showed a stronger connectivity between the pPaHCl and a cluster of voxels in the right uncus as well as FEFrDAN and a cluster of voxels in the right fusiform gyrus (p-FDR <0.05). MedFC and SMAr connectivity showed a negative correlation to suicidal ideation (p-FDR = 0.018). These findings suggest a possible role of altered resting state FC among brain regions in neurobiology of depressive disorder and suicidal ideation.

Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics

Identifying significant outliers or atypical objects from multimodal datasets is an essential and challenging issue for biomedical research. This problem is addressed, using the influence function of multiple kernel canonical correlation analysis. First, the influence function (IF) of the kernel mean element, the kernel covariance operator, the kernel cross-covariance operator and kernel canonical correlation analysis (kernel CCA) are studied. Second, an IF of multiple kernel CCA is proposed, which can be applied to multimodal datasets. Third, a visualization method is proposed to detect influential observations of multiple sources of data based on the IF of kernel CCA and multiple kernel CCA. Finally, to validate the method, experiments on both synthesized and imaging genetics data (e.g., SNP, fMRI, and DNA methylation) are performed. To examine the outliers, both the stem-and-leaf display and distribution based technique are used. The performance of the proposed approach is illustrated on 116 candidate regions of interest (ROIs) from the fMRI data of schizophrenia study to identify significant ROIs. The proposed method and two state-of-the-art statistical methods have identified 8, 34, and 10 ROIs, respectively. Based on an online database, the brain mappings of the selected common 7 ROIs indicate the irregular brain regions susceptible to schizophrenia. The results demonstrate that the proposed method is capable of analyzing outliers and the influence of observations, and can be applicable to many other biomedical data which are often high-dimensional and multi-modal.

Gender-Based Cerebral Perfusion Differences in 46,034 Functional Neuroimaging Scans.

Studies have reported that females have widespread increases in regional cerebral blood flow, but the studies were relatively small and inconsistent. Here we analyzed a healthy and a very large clinical psychiatric population to determine the effect of gender, using single photon emission computed tomography (SPECT). Whole brain and region of interest (ROI) gender differences were analyzed in a total of 46,034 SPECT scans at baseline and concentration. The sample included 119 healthy subjects and 26,683 patients (60.4% male, 39.6% female); a subset of 11,587 patients had complete diagnostic information. A total of 128 regions were analyzed according to the AAL Atlas, using ROI Extract and SPSS statistical software programs, controlling for age, diagnoses, and correcting for multiple comparisons. Compared to males, healthy females showed significant whole brain (p < 0.01) and ROI increases in 65 baseline and 48 concentration regions (p < 0.01 corrected). Healthy males showed non-significant increases in 9 and 22 regions, respectively. In the clinical group, there were widespread significant increases in females, especially in the prefrontal and limbic regions, and specific increases in males in the inferior occipital lobes, inferior temporal lobes, and lobule 7 and Crus 2 of the cerebellum. These findings were replicated in the subset of 11,587 patients with the effect of diagnoses removed. Our results demonstrated significant gender differences in a healthy and clinical population. Understanding these differences is crucial in evaluating functional neuroimaging and may be useful in understanding the epidemiological gender differences among psychiatric disorders.

Optical Coherence Tomography and Glaucoma Progression: A Comparison of a Region of Interest Approach to Average Retinal Nerve Fiber Layer Thickness.

To determine whether the change in the retinal nerve fiber layer (RNFL) thickness in a region of interest (ROI) is a better measure of glaucoma progression than the change in average circumpapillary (cp) RNFL thickness. Disc cube scans were obtained with frequency domain optical coherence tomography from 60 eyes of 60 patients (age, 61.7±12.7 y) with early or suspected glaucoma and controlled intraocular pressure. The average time between 2 test dates was 3.2±1.8 years. En-face images of the scans from the 2 tests were aligned based on the blood vessels, and cp images were derived for an annulus 100 μm wide and 3.4 mm in diameter, centered on the disc. An ROI was defined as the portion of the circumpapillary retinal nerve fiber layer (cpRNFL) plot within the temporal disc that extended below the 1% confidence interval for ≥5 degrees. Trend analysis using multilevel mixed-effects models was used to compare the rates of change between ROI width and average cpRNFL thickness. In total, 26 of the 60 eyes had a total of 33 ROIs. The ROI width significantly increased between the 2 test dates (median, 4.9 degrees; Q1=1.03 degrees, Q3=10.5 degrees). In comparison, the average cpRNFL thickness did not decrease significantly over the same period (median, -0.7 μm; Q1=-2.7 μm, Q3=2.7 μm). Mixed-effects linear models confirmed significant ROI progression (P=0.015), but not average cpRNFL (P=0.878). In this population, RNFL thinning in a ROI is a better measure of progression than is average cpRNFL thickness change.

Intelligent Watermarking for High-Capacity Low-Distortion Data Embedding

Image watermarking intends to hide secret data for the purposes of copyright protection, image authentication, data privacy, and broadcast monitoring. The ultimate goal is to achieve highest embedding capacity and lowest image distortion. In this paper, we present an intelligent watermarking scheme which can automatically analyze an image content to extract significant regions of interest (ROIs). A ROI is an area involving crucial information, and will be kept intact. The remaining regions of non-interest (RONIs) are collated for embedding watermarks, and will be ranked according to their entropy fuzzy memberships into a degree of importance. They are embedded by different amounts of bits to achieve optimal watermarking. The watermark is compressed and embedded in the frequency domain of the image. Experimental results show that the proposed technique has accomplished high capacity, high robustness, and high PSNR (peak signal-to-noise ratio) watermarking.

Relations among EEG-alpha asymmetry and positivity personality trait

The present study investigates cortical structures associated with personality dimension of positivity (POS) by using a standardized low-resolution brain electromagnetic tomography (sLORETA), which provides EEG localization measures that are independent of the recording reference. Resting EEG and self-report measures of positivity, self-esteem, life satisfaction, and optimism were collected from 51 female undergraduates. EEG was recorded across 29 scalp sites. Anterior and posterior source alpha asymmetries of cortical activation were obtained by using sLORETA. Based on previous research findings, 10 frontal and 6 parietal regions of interest (ROI) were derived. Alpha asymmetry in the posterior cingulate (i.e., BA23 and BA31) was uniquely associated with both POS scores. These areas are, hypothetically, part of a complex default-mode neural network (DMN). The activity in the DMN usually increases during tasks that invoke self-referential processing, such as responding to statements describing one’s personality, attitudes, or preferences. Importantly, the cortical structures associated with POS were different from those associated with indicators. Indeed, measures of “optimism” failed to maintain a significant correlation with any of the previously significant ROI, but “self-esteem” and “life satisfaction” revealed robust associations with alpha asymmetry at the precuneus (i.e., BA7), after controlling for POS residual scores. In conclusion: Present findings support the assumption that POS is a basic disposition that reflects the concerted activity of brain structures that are essential for integrating self-referential thought and autobiographical memories and for assigning a positive valence to one’s experience and attitude toward the future.

Optimizing methods for linking cinematic features to fMRI data

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film ‘At Land’ by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is – in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice – in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features.

C-12 * Working Memory and fMRI Correlates of Smoking Cessation Treatment Outcome

Objective: Smoking treatment may be improved by clarifying determinants of treatment success. Literature suggests that cognitive measures such as working memory (WM) performance are correlated with success. We hypothesized that WM performance and associated brain activity would predict treatment outcome. Method: Participants included 15 treatment-seeking community-recruited smokers (Mcigs/day = 20.9). Participants enrolled in a smoking-cessation program (nine psychotherapy sessions; 8 weeks of nicotine-replacement patches) and 53% successfully quit smoking. Treatment success was defined as not smoking daily following treatment (confirmed by exhaled CO). Verbal WM was assessed using a 2-back fMRI paradigm. Echoplanar BOLD-FMRI was conducted using a Siemens 3T system. A voxel-wise analysis was used to identify 2-back activation clusters for a functional Region of Interest (ROI) analyses (p20 voxels). Independent-samples t-tests were performed to identify differences between successful/unsuccessful quitters on 2-back performance and brain activity in task-associated regions. Results: Successful quitters exhibited significantly faster mean reaction time (t = −2.56, p = .023; 95% CI [−551.7−48.29]). The functional ROI analysis provided nine significant ROIs (2-back > baseline). Quitters exhibited significantly greater activation in the left precentral gyrus (t = 2.49, p = .027; 95% CI [0.149−2.103]) than unsuccessful quitters. Conclusion(s): Our results are consistent with prior research findings that cognitive persistence is a significant prognostic indicator of smoking cessation outcome. In our study, those who successfully quit smoking exhibited significantly higher scores on an effortful WM challenge, which is often presented to assess cognitive persistence. Further, increased activation in WM-associated brain regions during better performance supports the hypothesis they were exhibiting increased effort.