Jagath C. Rajapakse

Bio: Jagath C. Rajapakse is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Support vector machine & Image segmentation. The author has an hindex of 42, co-authored 260 publications receiving 10618 citations. Previous affiliations of Jagath C. Rajapakse include National Institutes of Health & University at Buffalo.

Quantitative Brain Magnetic Resonance Imaging in Attention-Deficit Hyperactivity Disorder

Abstract: Background: Anatomic magnetic resonance imaging (MRI) studies of attention-deficit hyperactivity disorder (ADHD) have been limited by small samples or measurement of single brain regions. Since the neuropsychological deficits in ADHD implicate a network linking basal ganglia and frontal regions, 12 subcortical and cortical regions and their symmetries were measured to determine if these structures best distinguished ADHD. Method: Anatomic brain MRIs for 57 boys with ADHD and 55 healthy matched controls, aged 5 to 18 years, were obtained using a 1.5-T scanner with contiguous 2-mm sections. Volumetric measures of the cerebrum, caudate nucleus, putamen, globus pallidus, amygdala, hippocampus, temporal lobe, cerebellum; a measure of prefrontal cortex; and related right-left asymmetries were examined along with midsagittal area measures of the cerebellum and corpus callosum. Interrater reliabilities were.82 or greater for all MRI measures. Results: Subjects with ADHD had a 4.7% smaller total cerebral volume ( P =.02). Analysis of covariance for total cerebral volume demonstrated a significant loss of normal right>left asymmetry in the caudate ( P =.006), smaller right globus pallidus ( P =.005), smaller right anterior frontal region ( P =.02), smaller cerebellum ( P =.05), and reversal of normal lateral ventricular asymmetry ( P =.03) in the ADHD group. The normal age-related decrease in caudate volume was not seen, and increases in lateral ventricular volumes were significantly diminished in ADHD. Conclusion: This first comprehensive morphometric analysis is consistent with hypothesized dysfunction of right-sided prefrontal-striatal systems in ADHD.

Quantitative Magnetic Resonance Imaging of Human Brain Development: Ages 4–18

Abstract: Brain magnetic resonance images (MRI) of 104 healthy children and adolescents, aged 4-18, showed significant effects of age and gender on brain morphometry! Males had larger cerebral (9%) and cerebellar (8%) volumes (P = 0.01 and 0.0001, respectively). In contrast, the cerebral hemispheres and caudate showed a highly consistent rightgreater-than-left asymmetry [P < 00001 for both). All volumes demonstrated a high degree of variability. These findings highlight gender-specific maturational changes of the developing brain and the need for large gender-matched samples in pediatric neuropsychiatric studies.

Quantitative MRI of the temporal lobe, amygdala, and hippocampus in normal human development: Ages 4–18 years

Abstract: The volume of the temporal lobe, superior temporal gyrus, amygdala, and hippocampus was quantified from magnetic images of the brains of 99 healthy children and adolescents aged 4-18 years. Variability in volume was high for all structures examined. When adjusted for a 9% larger total cerebral volume in males, there were no significant volume differences between sexes. However, sex-specific maturational changes were noted in the volumes of medial temporal structures, with the left amygdala increasing significantly only in males and with the right hippocampus increasing significantly only in females. Right-greater-than-left laterality effects were found for temporal lobe, superior temporal gyrus, amygdala, and hippocampal volumes. These results are consistent with previous preclinical and human studies that have indicated hormonal responsivity of these structures and extend quantitative morphologic findings from the adult literature. In addition to highlighting the need for large samples and sex-matched controls in pediatric neuroimaging studies, the information from this understudied age group may be of use in evaluating developmental hypotheses of neuropsychiatric disorders.

Statistical approach to segmentation of single-channel cerebral MR images

Abstract: A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are modeled by a finite Gaussian mixture. Smoothness and piecewise contiguous nature of the tissue regions are modeled by a three-dimensional (3-D) Markov random field (MRF). A segmentation algorithm, based on the statistical model, approximately finds the maximum a posteriori (MAP) estimation of the segmentation and estimates the model parameters from the image data. The proposed scheme for segmentation is based on the iterative conditional modes (ICM) algorithm in which measurement model parameters are estimated using local information at each site, and the prior model parameters are estimated using the segmentation after each cycle of iterations. Application of the algorithm to a sample of clinical MR brain scans, comparisons of the algorithm with other statistical methods, and a validation study with a phantom are presented. The algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of MR images in the presence of the random noise and intensity inhomogeneities.

Sexual dimorphism of the developing human brain

Abstract: 1. Sexual dimorphism of human brain anatomy has not been well-studied between 4 and 18 years of age, a time of emerging sex differences in behavior and the sexually specific hormonal changes of adrenarche (the predominantly androgenic augmentation of adrenal cortex function occurring at approximately age 8) and puberty. 2. To assess sex differences in brain structures during this developmental period volumes of the cerebrum, lateral ventricles, caudate, putamen, globus pallidus temporal lobe, amygdala, and hippocampus, and midsagittal area measurements of the corpus callosum were quantified from brain magnetic resonance images of 121 healthy children and adolescent and examined in relation to age and sex. 3. Males had a 9% larger cerebral volume. When adjusted for cerebral volume by ANCOVA only the basal ganglia demonstrated sex differences in mean volume with the caudate being relatively larger in females and the globus pallidus being relatively larger in males. The lateral ventricles demonstrated a prominent sex difference in brain maturation with robust increases in size in males only. A piecewise-linear model revealed a significant change in the linear regression slope of lateral ventricular volume in males after age 11 that was not shared by females at that or other ages. 4. Amygdala and hippocampal volume increased for both sexes but with the amygdala increasing significantly more in males than females and hippocampal volume increasing more in females. 5. These sexually dimorphic patterns of brain development may be related to the observed sex differences in age of onset, prevalence, and symptomatology seen in nearly all neuropsychiatric disorders of childhood.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).