Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses

Real Space Approach to Electronic-Structure Calculations

We have applied the Finite Element Method to the self-consistent electronic structure calculations of molecules and solids for the first time. In this approach all the calculations are performed in "real space" and the use of non-uniform mesh is made possible, thus enabling us to deal with localized systems with ease. To illustrate the utility of this method, we perform an all-electron calculation of hydrogen molecule in a supercell with LDA approximation. Our method is also applicable to mesoscopic systems.Comment: 11 pages, LaTeX, 5 figures available on request from [email protected]

Re-operation of idiopathic full-thickness macular holes after initial surgery with internal limiting membrane peel

Background/aims: A retrospective consecutive case series to evaluate the efficacy of re-operation in patients with persistent or recurrent idiopathic full-thickness macular hole after initial surgery with internal limiting membrane peel (ILM). Methods: 491 patients underwent surgery for fullthickness macular hole from January 2004 to November 2007. Fifty-five patients either did not close or reopened during the follow-up period. Thirty patients with initial ILM peel underwent repeat surgery involving vitrectomy, enlargement of ILM rhexis and gas tamponade. Results: Anatomical closure rate was 88.8% for primary surgery and 46.7% (14/30) for re-operation. There was a statistically significant improvement in overall best corrected visual acuity (BCVA) from re-operation baseline BCVA (p=0.02) within 1 year. For holes that did not close after the second surgery, visual acuity did not worsen. Conclusion: Re-operation has a reduced success rate of anatomical closure. However, BCVA is statistically significantly improved from re-operation baseline, so even though we cannot return vision to pre-pathological baseline, re-operation can improve on this new baseline.Link_to_subscribed_fulltex

Retooling computational techniques for EEG-based neurocognitive modeling of children's data, validity and prospects for learning and education

This paper describes continuing research on the building of neurocognitive models of the internal mental and brain processes of children using a novel adapted combination of existing computational approaches and tools, and using electro-encephalographic (EEG) data to validate the models. The guiding working model which was pragmatically selected for investigation was the established and widely used Adaptive Control of Thought-Rational (ACT-R) modeling architecture from cognitive science. The anatomo-functional circuitry covered by ACT-R is validated by MRI-based neuroscience research. The present experimental data was obtained from a cognitive neuropsychology study involving preschool children (aged 46), which measured their visual selective attention and word comprehension behaviors. The collection and analysis of Event-Related Potentials (ERPs) from the EEG data allowed for the identification of sources of electrical activity known as dipoles within the cortex, using a combination of computational tools (Independent Component Analysis, FASTICA; EEG-Lab DIPFIT). The results were then used to build neurocognitive models based on Python ACT-R such that the patterns and the timings of the measured EEG could be reproduced as simplified symbolic representations of spikes, built through simplified electric-field simulations. The models simulated ultimately accounted for more than three-quarters of variations spatially and temporally in all electrical potential measurements (fit of model to dipole data expressed as R 2 ranged between 0.75 and 0.98; P < 0.0001). Implications for practical uses of the present work are discussed for learning and educational applications in non-clinical and special needs children's populations, and for the possible use of non-experts (teachers and parents)

Cognitive control, bedtime patterns, and testing time in female adolescent students: behavioral and neuro-electrophysiological correlates

IntroductionShorter and/or disrupted sleep during adolescence is associated with cognitive and mental health risks, particularly in females. We explored the relationship between bedtime behavior patterns co-varying with Social Jet Lag (SJL) and School Start Times (SST) and neurocognitive performance in adolescent female students.MethodsTo investigate whether time of day (morning vs. afternoon), early SSTs and days of the school week can be correlated with neurocognitive correlates of sleep insufficiency, we recruited 24 female students aged 16–18 to report sleep logs, and undergo event-related electroencephalographic recordings on Monday, Wednesday, mornings, and afternoons. Using a Stroop task paradigm, we analyzed correlations between reaction times (RTs), accuracy, time of day, day of week, electroencephalographic data, and sleep log data to understand what relationships may exist.ResultsParticipants reported a 2-h sleep phase delay and SJL. Stroop interference influenced accuracy on Monday and Wednesday similarly, with better performance in the afternoon. For RTs, the afternoon advantage was much larger on Monday than Wednesday. Midline Event-Related Potentials (ERPs) yielded higher amplitudes and shorter latencies on Wednesday morning and Monday afternoon, in time windows related to attention or response execution. A notable exception were delayed ERP latencies on Wednesday afternoon. The latter could be explained by the fact that delta EEG waves tended to be the most prominent, suggesting heightened error monitoring due to accumulating mental fatigue.DiscussionThese findings provide insights into the interaction between SJL and SST and suggest evidence-based criteria for planning when female adolescents should engage in cognitive-heavy school activities such as tests or exams

Retooling Computational Techniques for EEG-Based Neurocognitive Modeling of Children's Data, Validity and Prospects for Learning and Education

This paper describes continuing research on the building of neurocognitive models of the internal mental and brain processes of children using a novel adapted combination of existing computational approaches and tools, and using electro-encephalographic (EEG) data to validate the models. The guiding working model which was pragmatically selected for investigation was the established and widely used Adaptive Control of Thought-Rational (ACT-R) modeling architecture from cognitive science. The anatomo-functional circuitry covered by ACT-R is validated by MRI-based neuroscience research. The present experimental data was obtained from a cognitive neuropsychology study involving preschool children (aged 4–6), which measured their visual selective attention and word comprehension behaviors. The collection and analysis of Event-Related Potentials (ERPs) from the EEG data allowed for the identification of sources of electrical activity known as dipoles within the cortex, using a combination of computational tools (Independent Component Analysis, FASTICA; EEG-Lab DIPFIT). The results were then used to build neurocognitive models based on Python ACT-R such that the patterns and the timings of the measured EEG could be reproduced as simplified symbolic representations of spikes, built through simplified electric-field simulations. The models simulated ultimately accounted for more than three-quarters of variations spatially and temporally in all electrical potential measurements (fit of model to dipole data expressed as R2 ranged between 0.75 and 0.98; P &lt; 0.0001). Implications for practical uses of the present work are discussed for learning and educational applications in non-clinical and special needs children's populations, and for the possible use of non-experts (teachers and parents)

High-Resolution Optical Coherence Tomography Retinal Imaging: A Case Series Illustrating Potential and Limitations

Purpose. To present a series of retinal disease cases that were imaged by spectral domain optical coherence tomography (SD-OCT) in order to illustrate the potential and limitations of this new imaging modality. Methods. The series comprised four selected cases (one case each) of age-related macular degeneration (ARMD), diabetic retinopathy (DR), central retinal artery occlusion (CRAO), and branch retinal vein occlusion (BRVO). Patients were imaged using the Heidelberg Spectralis (Heidelberg Engineering, Germany) in SD-OCT mode. Patients also underwent digital fundus photography and clinical assessment. Results. SD-OCT imaging of a case of age-related macular degeneration revealed a subfoveal choroidal neovascular membrane with detachment of the retinal pigment epithelium (RPE) and neurosensory retina. Using SD-OCT, the cases of DR and BRVO both exhibited macular edema with cystoid spaces visible in the outer retina. Conclusions. The ability of SD-OCT to clearly and objectively elucidate subtle morphological changes within the retinal layers provides information that can be used to formulate diagnoses with greater confidence

Deformation-based shape analysis of the hippocampus in the semantic variant of primary progressive aphasia and Alzheimer’s disease

Background Increasing evidence shows that the semantic variant of primary progressive aphasia (svPPA) is characterized by hippocampal atrophy. However, less is known about disease-related morphological hippocampal changes. The goal of the present study is to conduct a detailed characterization of the impact of svPPA on global hippocampus volume and morphology compared with control subjects and patients with Alzheimer’s disease (AD). Methods We measured hippocampal volume and deformation-based shape differences in 22 patients with svPPA compared with 99 patients with AD and 92 controls. Multiple Automatically Generated Templates Brain Segmentation Algorithm (MAGeT-Brain) was used on MRI images obtained at the diagnostic visit. Results Comparable left and right hippocampal atrophy were observed in svPPA and AD. Deformation-based shape analysis showed a common pattern of morphological deformation in svPPA and AD compared with controls. More specifically, both svPPA and AD showed inward deformations in the dorsal surface of the hippocampus, from head to tail on the left side, and more limited to the anterior portion of the body in the right hemisphere. These results also pointed out that both diseases are characterized by a lateral displacement of the central part (body) of the hippocampus. Discussion Our study provides critical new evidence of hippocampal morphological changes in svPPA, similar to those found in AD. These findings highlight the importance of considering morphological hippocampal changes as part of the anatomical profile of patients with svPPA

MRI atlas of a lizard brain

Magnetic resonance imaging (MRI) is an established technique for neuroanatomical analysis, being particularly useful in the medical sciences. However, the application of MRI to evolutionary neuroscience is still in its infancy. Few magnetic resonance brain atlases exist outside the standard model organisms in neuroscience and no magnetic resonance atlas has been produced for any reptile brain. A detailed understanding of reptilian brain anatomy is necessary to elucidate the evolutionary origin of enigmatic brain structures such as the cerebral cortex. Here, we present a magnetic resonance atlas for the brain of a representative squamate reptile, the Australian tawny dragon (Agamidae: Ctenophorus decresii), which has been the subject of numerous ecological and behavioral studies. We used a high-field 11.74T magnet, a paramagnetic contrasting-enhancing agent and minimum-deformation modeling of the brains of thirteen adult male individuals. From this, we created a high-resolution three-dimensional model of a lizard brain. The 3D-MRI model can be freely downloaded and allows a better comprehension of brain areas, nuclei, and fiber tracts, facilitating comparison with other species and setting the basis for future comparative evolution imaging studies. The MRI model and atlas of a tawny dragon brain (Ctenophorus decresii) can be viewed online and downloaded using the Wiley Biolucida Server at wiley.biolucida.net.Government of Australia, Grant/Award Numbers: APA#31/2011, IPRS#1182/2010; National Science and Engineering Research Council of Canada, Grant/Award Number: PGSD3-415253-2012; Quebec Nature and Technology Research Fund, Grant/AwardNumber: 208332; National Imaging Facility of Australia; Spanish Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional, Grant/Award Number:BFU2015-68537-

Inter- and intra-individual variation in brain structural-cognition relationships in aging

The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ±4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure