Inhibitory control/regulation is critical to adapt behavior in accordance with changing environmental circumstances. Dysfunctional inhibitory regulation is ubiquitous in neurological and psychiatric populations. These populations exhibit dysfunction across psychological domains, including memory/thought, emotion/affect, and motor response. Although investigation examining inhibitory regulation within a single domain has begun outlining the basic neural mechanisms supporting regulation, it is unknown how the neural mechanisms of these domains interact. To investigate the organization of inhibitory neural networks within and across domains, we used neuroimaging to outline the functional and anatomical pathways that comprise inhibitory neural networks regulating cognitive, emotional, and motor processes. Networks were defined at the group level using an array of analyses to indicate their intrinsic pathway structure, which was subsequently assessed to determine how the pathways explained individual differences in behavior. Results reveal how neural networks underlying inhibitory regulation are organized both within and across domains, and indicate overlapping/common neural elements.

A longitudinal experiment was conducted to evaluate the effectiveness of new methods for learning neuroanatomy with computer-based instruction. Using a 3D graphical model of the human brain, and sections derived from the model, tools for exploring neuroanatomy were developed to encourage adaptive exploration. This is an instructional method which incorporates graphical exploration in the context of repeated testing and feedback. With this approach, 72 participants learned either sectional anatomy alone or whole anatomy followed by sectional anatomy. Sectional anatomy was explored either with perceptually continuous navigation through the sections or with discrete navigation (as in the use of an anatomical atlas). Learning was measured longitudinally to a high performance criterion. Subsequent tests examined transfer of learning to the interpretation of biomedical images and long-term retention. There were several clear results of this study. On initial exposure to neuroanatomy, whole anatomy was learned more efficiently than sectional anatomy. After whole anatomy was mastered, learners demonstrated high levels of transfer of learning to sectional anatomy and from sectional anatomy to the interpretation of complex biomedical images. Learning whole anatomy prior to learning sectional anatomy led to substantially fewer errors overall than learning sectional anatomy alone. Use of continuous or discrete navigation through sectional anatomy made little difference to measured outcomes. Efficient learning, good long-term retention, and successful transfer to the interpretation of biomedical images indicated that computer-based learning using adaptive exploration can be a valuable tool in instruction of neuroanatomy and similar disciplines.

No abstract

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI).

Using interactive computer-based methods of instruction, this research examined the contribution of whole (3D) anatomical knowledge to learning sectional anatomy. Participants either learned sectional anatomy alone or learned whole anatomy prior to learning sectional anatomy. Sectional anatomy was explored either with perceptually continuous navigation or discretely, as in the use of an anatomical atlas. Learning occurred over repeated cycles of study, test, and feedback, and continued to a high performance criterion. After learning, transfer of knowledge to interpreting biomedical images and long-term retention were tested. Whole anatomy was learned quickly and transferred well to the learning of sectional anatomy: initial accuracy was higher, learning of sectional anatomy was completed more rapidly, and there was less error over the entire course of learning. Knowledge of whole anatomy benefited the long-term retention of sectional anatomy at 2-3 weeks. Learners demonstrated high levels of transfer to the interpretation of biomedical images.

This article reports large item effects in a study of computer-based learning of neuroanatomy. Outcome measures of the efficiency of learning, transfer of learning, and generalization of knowledge diverged by a wide margin across test items, with certain sets of items emerging as particularly difficult to master. In addition, the outcomes of comparisons between instructional methods changed with the difficulty of the items to be learned. More challenging items better differentiated between instructional methods. This set of results is important for two reasons. First, it suggests that instruction may be more efficient if sets of consistently difficult items are the targets of instructional methods particularly suited to them. Second, there is wide variation in the published literature regarding the outcomes of empirical evaluations of computer-based instruction. As a consequence, many questions arise as to the factors that may affect such evaluations. The present paper demonstrates that the level of challenge in the material that is presented to learners is an important factor to consider in the evaluation of a computer-based instructional system.

Whole genome sequencing (WGS) of Mycobacterium tuberculosis has been constructive in understanding its evolution, genetic diversity and the mechanisms involved in drug resistance. A large number of sequencing efforts from across the globe have revealed genetic diversity among clinical isolates and the genetic determinants for their resistance to anti-tubercular drugs. Considering the high TB burden in India, the availability of WGS studies is limited. Here we present, WGS results of 200 clinical isolates of M. tuberculosis from North India which are categorized as sensitive to first-line drugs, mono-resistant, multi-drug resistant and pre-extensively drug resistant isolates. WGS revealed that 20% of the isolates were co-infected with M. tuberculosis and non-tuberculous mycobacteria species. We identified 12,802 novel genetic variations in M. tuberculosis isolates including 343 novel SNVs in 38 genes which are known to be associated with drug resistance and are not currently used in the diagnostic kits for detection of drug resistant TB. We also identified M. tuberculosis lineage 3 to be predominant in the northern region of India. Additionally, several novel SNVs, which may potentially confer drug resistance were found to be enriched in the drug resistant isolates sampled. This study highlights the significance of employing WGS in diagnosis and for monitoring further development of MDR-TB strains.

The study demonstrated the presence of viable strains of M. leprae in skin smear samples of paucibacillary patients and multibacillary patients, as well as in the environmental samples obtained from around their houses. This could play an important role in the continued transmission of leprosy.