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This article provides a step by step process of executing analytical or computer based Design for Six Sigma using a Sliding Door project as an example. It comprises of identification of Voice Of the Customer (VOC), transformation of VOC to what it is called Critical To Quality characteristics (CTQs), modeling of system transfers function, optimal and robust solutions, and tolerance design approach

Se realizó un estudio descriptivo de series de casos, en el Hospital “Dr. Ernesto Guevara de la Serna” de Las Tunas, con el objetivo de caracterizar los aspectos clínicos endoscópicos de la diverticulosis del colon, en el periodo comprendido desde enero 2011 a diciembre 2012. El universo quedó constituido por todos los pacientes a los que se les realizó colonoscopia, y la muestra por todos los pacientes con diagnóstico endoscópico de enfermedad diverticular. Para la obtención de los datos primarios se revisaron los expedientes clínicos de 101 pacientes con esta enfermedad, efectuando un cuestionario, en el cual se recogieron, analizaron y tabularon las variables, utilizando como medida de resumen el porcentaje. Se procesaron los datos y se interpretaron los resultados, concluyendo que en el grupo de pacientes estudiados predominaron las mujeres de más de 65 años, con dolor abdominal como síntoma principal y las hemorroides, como enfermedad que más se asoció a esta padecimiento, siendo el colon sigmoides el más afectado.

We enhanced DFSS Characterize and Optimize Phases to deal with multiple and functional response optimization. The enhancement basically starts with a development of functional meta-models of CAE outputs that are fast-to-compute and accurate enough within a certain design space. Having developed the multiple and functional meta-models, the influence of design variables to the functional responses are then obtained via visualization and sensitivity analysis based on Sobol's index. Multi-objective optimization is finally applied to search for design variable settings that give optimal functional responses. Abstract in Bahasa Indonesia : Di artikel ini kita memperkaya fase-fase Characterize and Optimize dari design for six sigma (DFSS) methodology untuk bisa menyelesaikan rekayasa produk dan proses kompleks yang mengandung banyak respons fungsional. Pemerkayaan dimulai dengan pembangunan meta-model fungsional dari keluaran computer aided engineering (CAE) yang diperoleh lewat rancangan eksperimental. Kemudian, dilanjutkan dengan pembangunan sebuah algoritma untuk mengidentifikasi tingkat pengaruh dari variabel-variable rancangan ke respons fungsional secara visual maupun analitis. Akhirnya, optimasi multi obyektif diterapkan untuk mencari nilai-nilai variabel design yang mampu memberikan respons fungsional secara optimal dan tangguh. Kata-kunci: perancangan berbasis komputer, response fungsional.

El cáncer colorrectal es uno de los tumores malignos más frecuentes. Se realizó un estudio descriptivo, longitudinal y prospectivo, con los pacientes diagnosticados de cáncer de colon en el municipio de Las Tunas. La población de estudio estuvo constituida por los 152 pacientes con diagnóstico de esta entidad, incluidos hasta el cierre de enero de 2013 en el registro de enfermedades comunes de la provincia y el registro provincial del cáncer. A todos se les realizaron entrevistas para obtener una descripción de los antecedentes familiares y el grado de parentesco, los grupos de edad más afectados y las características clínicas de los pacientes, entre otras. Se les realizó videocolonoscopia, determinando las localizaciones más frecuentes y el tipo histológico endoscópico. El estudio de videocolonoscopia fue indicado con frecuencia en las edades tardías de la vida y el signo más frecuente de indicación del proceder fue la rectorragia. Solo 26 pacientes tuvieron un familiar de primer grado de parentesco diagnosticado con la misma enfermedad. La localización más frecuente del tumor fue en la unión rectosigmoidea e histológicamente tuvo elevada incidencia el adenocarcinoma

Se realizó un estudio observacional descriptivo, con el objetivo de caracterizar los pacientes con cirrosis hepática (CH), atendidos en el servicio de gastroenterología del Hospital General Docente “Dr. Ernesto Guevara de la Serna”, Las Tunas, Cuba, en el período comprendido entre febrero de 2012 y febrero de 2014. Se estudiaron 55 pacientes con cirrosis hepática diagnosticada por laparoscopia. Predominaron los pacientes del grupo de edad entre 50 y 59 años, siendo el sexo masculino el más afectado. Las principales manifestaciones clínicas que se apreciaron en estos pacientes fueron la ascitis y los edemas en miembros inferiores, así como las complicaciones más frecuentemente encontradas, la ascitis y la hemorragia digestiva alta. El alcoholismo y la etiología viral por virus C se presentaron con mayor frecuencia, predominando el primero. En el mayor por ciento de los pacientes debutó la enfermedad con al menos una complicación

Se realizó un estudio descriptivo de corte transversal, con el objetivo de caracterizar los pacientes atendidos por adenocarcinoma gástrico en el Hospital General Docente “Dr. Ernesto Guevara de la Serna” de provincia de Las Tunas, durante el periodo comprendido entre el 1 de enero de 2013 y el 1 de enero de 2014. El universo quedó constituido por todos los pacientes a los que se les realizó biopsia de estómago, por lesión sospechosa de adenocarcinoma gástrico, y la muestra por todos los pacientes con diagnóstico histológico de esta enfermedad. Para la obtención de los datos primarios se revisaron los expedientes clínicos de 23 pacientes con biopsia positiva. A partir de un cuestionario se recogieron y procesaron los datos y se interpretaron los resultados. Se encontró un predominio en el sexo masculino (69,5%), en los mayores de 51 años (30,4%) y en la población de raza negra (47,8%) con antecedentes de infección por helicobacter pylori (69,5%). El 43,4% de los pacientes debutó con una dispepsia prolongada, como síntoma predominante, y la imagen endoscópica más observada fue la úlcera cancerosa (12 casos), en relación con el patrón histológico difuso

Innovation is the way of life of any institution to profitably sustain its life. It starts with empathy, the ability to reach outside of ourselves and walk in someone else’s shoes, and optimal implementation of the newly advanced technology. Innovation shows its results through continuously hard working efforts known as "10 Thousand Hours Rule". As world uncertainty creates complexity we, instead of predicting, should therefore anticipate the future by creating and managing real options on contingent projects or elements of alternative optimal strategies. This should reflect into our portfolio strategy. Abstract in Bahasa Indonesia: Inovasi merupakan darah bagi suatu institusi untuk bisa hidup berkelanjutan serta menguntungkan. Inovasi berupa penemuan baru secara sistematis yang berawal dari empati, kemampuan untuk melihat dunia melalui mata orang lain, dan pemanfaatan secara optimal kemajuan teknologi yang ada. Inovasi baru menghasilkan buahnya melalui kerja keras, yaitu dengan mengikuti “Aturan 10 Ribu Jam” secara berkesinambungan. Ketidakpastian, interaksi, keterbatasan dan degradasi menciptakan kompleksitas tentang kebutuhan dan solusi di masa depan. Oleh sebab itu daripada meramalkan risiko yang bakal terjadi, kita sebaiknya memasang strategi berupa skenario untuk mereduksi akibat dari risiko masa depan yang tidak kita mengerti. Skenario ini dapat diperoleh lewat penciptaan dan penanganan beberapa pilihan nyata atas semua proyek antisipatif yang ada. Kata kunci: Inovasi, ketidakpastian dan kompleksitas, aturan 10 ribu jam, paradoks strategi, peta jalan, empati, kerja berkesinambungan.

Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46-96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions., Competing Interests: Disclosure statement The authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (r s  = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (r s  = 0.41), and many outlier WMH volumes were detected when exploring within-person trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: r s  = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Magnetic Resonance Imaging (MRI) studies have shown that cortical volume declines with age. Although volume is a multiplicative measure consisting of thickness and area, few studies have focused on both its components. Information on decline variability and associations between person-specific changes of different brain metrics, brain regions, and cognition is sparse. In addition, the estimates have often been biased by the measurement error, because three repeated measures are minimally required to separate the measurement error from person-specific changes. With a sample size of N = 231, five repeated measures, and an observational time span of seven years, this study explores the associations between changes of different brain metrics, brain regions, and cognitive abilities in aging. Person-specific changes were obtained by latent growth curve models using Bayesian estimation. Our data indicate that both thickness and area are important contributors to volumetric changes. In most brain regions, area clearly declined on average over the years, while thickness showed only little decline. However, there was also substantial variation around the average slope in thickness and area. The correlation pattern of changes in thickness between brain regions was strong and largely homogenous. The pattern for changes in area was similar but weaker, indicating that factors affecting area may be more region-specific. Changes in thickness and volume were substantially correlated with changes in cognition. In some brain regions, changes in area were also related to changes in cognition. Overall, studying the associations between the trajectories of brain regions in different brain metrics provides insights into the regional heterogeneity of structural changes. SIGNIFICANCE STATEMENT: Many studies have described volumetric brain changes in aging. Few studies have focused on both its individual components: area and thickness. Longitudinal studies with three or more time points are highly needed, because they provide more precise average change estimates and, more importantly, allow us to quantify the associations between changes in the different brain metrics, brain regions, and other variables (e.g. cognitive abilities). Studying these associations is important because they can provide information regarding possible underlying factors of these changes. Our study, with a large sample size, five repeated measures, and an observational time span of seven years, provides new insights about the associations between person-specific changes in thickness, area, volume, and cognitive abilities., (Copyright © 2021. Published by Elsevier Inc.)

Cross-sectional studies indicate that normal aging is accompanied by decreases in brain structure. Longitudinal studies, however, are relatively rare and inconsistent regarding their outcomes. Particularly the heterogeneity of methods, sample characteristics and the high inter-individual variability in older adults prevent the deduction of general trends. Therefore, the current study aimed to compare longitudinal age-related changes in brain structure (measured through cortical thickness) in two large independent samples of healthy older adults ( n = 161 each); the Longitudinal Healthy Aging Brain (LHAB) database project at the University of Zurich, Switzerland, and 1000BRAINS at the Research Center Juelich, Germany. Annual percentage changes in the two samples revealed stable to slight decreases in cortical thickness over time. After correction for major covariates, i.e., baseline age, sex, education, and image quality, sample differences were only marginally present. Results suggest that general trends across time might be generalizable over independent samples, assuming the same methodology is used, and similar sample characteristics are present., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Jockwitz, Mérillat, Liem, Oschwald, Amunts, Jäncke and Caspers.)

Substantial evidence indicates that cognitive training can be efficacious for older adults, but findings regarding training-related brain plasticity have been mixed and vary depending on the imaging modality. Recent years have seen a growth in recognition of the importance of large-scale brain networks on cognition. In particular, task-induced deactivation within the default mode network (DMN) is thought to facilitate externally directed cognition, while aging-related decrements in this neural process are related to reduced cognitive performance. It is not yet clear whether task-induced deactivation within the DMN can be enhanced by cognitive training in the elderly. We previously reported durable cognitive improvements in a sample of healthy older adults (age range = 60-75) who completed 6 weeks of process-based object-location memory training ( N = 36) compared to an active control training group ( N = 31). The primary aim of the current study is to evaluate whether these cognitive gains are accompanied by training-related changes in task-related DMN deactivation. Given the evidence for heterogeneity of the DMN, we examine task-related activation/deactivation within two separate DMN branches, a ventral branch related to episodic memory and a dorsal branch more closely resembling the canonical DMN. Participants underwent functional magnetic resonance imaging (fMRI) while performing an untrained object-location memory task at four time points before, during, and after the training period. Task-induced (de)activation values were extracted for the ventral and dorsal DMN branches at each time point. Relative to visual fixation baseline: (i) the dorsal DMN was deactivated during the scanner task, while the ventral DMN was activated; (ii) the object-location memory training group exhibited an increase in dorsal DMN deactivation relative to the active control group over the course of training and follow-up; (iii) changes in dorsal DMN deactivation did not correlate with task improvement. These results indicate a training-related enhancement of task-induced deactivation of the dorsal DMN, although the specificity of this improvement to the cognitive task performed in the scanner is not clear., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mikos, Malagurski, Liem, Mérillat and Jäncke.)

This study used structural magnetic resonance imaging to examine whether specific anatomical features of Broca's and Wernicke's areas are related to language functions in typically developing older subjects with no specific language expertize. Data from 231 subjects from the Zurich LHAB-study are used for this study. For these subjects, we obtained several psychometric measures from which we calculated performance measures reflecting specific psychological functions (language comprehension, verbal fluency, perceptual speed, visual memory, recognition of regularities, and logical thinking). From the MRI measurements, we calculated the cortical thickness and cortical surface of Broca's and Wernicke's areas. Applying multiple regression analyses, we identified a moderately strong relationship between language comprehension and the brain metrics from Broca's and Wernicke's areas and showed that approximately 10% of the variance in language comprehension performance is explained by the linear combination of all perisylvian brain metrics. The other psychological functions (verbal fluency, perceptual speed, visual memory, recognition of regularities, and logical thinking) are not related to these brain metrics. Subsequent detailed analyses revealed that the cortical thickness of Wernicke's area, in particular, contributed most to this structure-function relationship. The better performance in the language comprehension tests was related to a thicker cortex in Wernicke's area. Thus, this study demonstrates a structure-function relationship between the anatomical features of the perisylvian language areas and language comprehension, suggesting that particular anatomical features are associated with better language performance., (© 2020 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Markers of cerebral small vessel disease (CSVD) have previously been associated with age-related cognitive decline. Using longitudinal data of cognitively healthy, older adults (N = 216, mean age at baseline = 70.9 years), we investigated baseline status and change in white matter hyperintensities (WMH) (total, periventricular, deep), normal appearing white matter (NAWM), brain parenchyma volume (BPV) and processing speed over seven years as well as the impact of different covariates by applying latent growth curve (LGC) models. Generally, we revealed a complex pattern of associations between the different CSVD markers. More specifically, we observed that changes of deep WMH (dWMH), as compared to periventricular WMH (pWMH), were more strongly related to the changes of other CSVD markers and also to baseline processing speed performance. Further, the number of lacunes rather than their volume reflected the severity of CSVD. With respect to the studied covariates, we revealed that higher education had a protective effect on subsequent total WMH, pWMH, lacunar number, NAWM volume, and processing speed performance. The indication of antihypertensive drugs was associated with lower lacunar number and volume at baseline and the indication of antihypercholesterolemic drugs came along with higher processing speed performance at baseline. In summary, our results confirm previous findings, and extend them by providing information on true within-person changes, relationships between the different CSVD markers and brain-behavior associations. The moderate to strong associations between changes of the different CSVD markers indicate a common pathological relationship and, thus, support multidimensional treatment strategies., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Healthy aging is associated with changes in cognitive performance and functional brain organization. In fact, cross-sectional studies imply lower modularity and significant heterogeneity in modular architecture across older subjects. Here, we used a longitudinal dataset consisting of four occasions of resting-state-fMRI and cognitive testing (spanning 4 years) in 150 healthy older adults. We applied a graph-theoretic analysis to investigate the time-evolving modular structure of the whole-brain network, by maximizing the multilayer modularity across four time points. Global flexibility, which reflects the tendency of brain nodes to switch between modules across time, was significantly higher in healthy elderly than in a temporal null model. Further, global flexibility, as well as network-specific flexibility of the default mode, frontoparietal control, and somatomotor networks, were significantly associated with age at baseline. These results indicate that older age is related to higher variability in modular organization. The temporal metrics were not associated with simultaneous changes in processing speed or learning performance in the context of memory encoding. Finally, this approach provides global indices for longitudinal change across a given time span and it may contribute to uncovering patterns of modular variability in healthy and clinical aging populations., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Describing the trajectories of age-related change for different brain structures has been of interest in many recent studies. However, our knowledge regarding these trajectories and their associations is still limited due to small sample sizes and low numbers of repeated measures. For the present study, we used a large longitudinal dataset (four measurements over 4 years) comprising anatomical data from a sample of healthy older adults ( N = 231 at baseline). This dataset enables us to gain new insights about volumetric cortical and subcortical changes and their associations in the context of healthy aging. Brain structure volumes were derived from T1-weighted MRI scans using FreeSurfer segmentation tools. Brain structure trajectories were fitted using mixed models and latent growth curve models to gain information about the mean extent and variability of decline trajectories for different brain structures as well as the associations between individual trajectories. On the group level, our analyses indicate similar linear changes for frontal and parietal brain regions, while medial temporal regions showed an accelerated decline with advancing age. Regarding subcortical regions, some structures showed strong declines (e.g., hippocampus), others showed little decline (e.g., pallidum). Our data provide little evidence for sex differences regarding the aforementioned trajectories. Between-person variability of the person-specific slopes (random slopes) was largest in subcortical and medial temporal brain structures. When looking at the associations between the random slopes from each brain structure, we found that the decline is largely homogenous across the majority of cortical brain structures. In subcortical and medial temporal brain structures, however, more heterogeneity of the decline was observed, meaning that the extent of the decline in one structure is less predictive of the decline in another structure. Taken together, our study contributes to enhancing our understanding of structural brain aging by demonstrating (1) that average volumetric change differs across the brain and (2) that there are regional differences with respect to between-person variability in the slopes. Moreover, our data suggest (3) that random slopes are highly correlated across large parts of the cerebral cortex but (4) that some brain regions (i.e., medial temporal regions) deviate from this homogeneity., (Copyright © 2020 Sele, Liem, Mérillat and Jäncke.)

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

Healthy aging is associated with weaker functional connectivity within resting state brain networks and stronger functional interaction between these networks. This phenomenon has been characterized as reduced functional segregation and has been investigated mainly in cross-sectional studies. Here, we used a longitudinal dataset which consisted of four occasions of resting state fMRI and psychometric cognitive ability data, collected from a sample of healthy older adults (baseline N = 232, age range: 64-87 y, age M = 70.8 y), to investigate the functional segregation of several well-defined resting state networks encompassing the whole brain. We characterized the ratio of within-network and between-network correlations via the well-established segregation index. Our findings showed a decrease over a 4-year interval in the functional segregation of the default mode, frontoparietal control and salience ventral attention networks. In contrast, we showed an increase in the segregation of the limbic network over the same interval. More importantly, the rate of change in functional segregation of the frontoparietal control network was associated with the rate of change in processing speed. These findings support the hypothesis of functional dedifferentiation in healthy aging as well as its role in cognitive function in elderly., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals., Competing Interests: DE, OK, DS, GL, FL, AG No competing interests declared, GV Reviewing editor, eLife, (© 2020, Engemann et al.)

Much of our behaviour is driven by two motivational dimensions-approach and avoidance. These have been related to frontal hemispheric asymmetries in clinical and resting-state EEG studies: Approach was linked to higher activity of the left relative to the right hemisphere, while avoidance was related to the opposite pattern. Increased approach behaviour, specifically towards unhealthy foods, is also observed in obesity and has been linked to asymmetry in the framework of the right-brain hypothesis of obesity. Here, we aimed to replicate previous EEG findings of hemispheric asymmetries for self-reported approach/avoidance behaviour and to relate them to eating behaviour. Further, we assessed whether resting fMRI hemispheric asymmetries can be detected and whether they are related to approach/avoidance, eating behaviour and BMI. We analysed three samples: Sample 1 (n = 117) containing EEG and fMRI data from lean participants, and Samples 2 (n = 89) and 3 (n = 152) containing fMRI data from lean, overweight and obese participants. In Sample 1, approach behaviour in women was related to EEG, but not to fMRI hemispheric asymmetries. In Sample 2, approach/avoidance behaviours were related to fMRI hemispheric asymmetries. Finally, hemispheric asymmetries were not related to either BMI or eating behaviour in any of the samples. Our study partly replicates previous EEG findings regarding hemispheric asymmetries and indicates that this relationship could also be captured using fMRI. Our findings suggest that eating behaviour and obesity are likely to be mediated by mechanisms not directly relating to frontal asymmetries in neuronal activation quantified with EEG and fMRI., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.

Aging is a known non-modifiable risk factor for stroke. Usually, this refers to chronological rather than biological age. Biological brain age can be estimated based on cortical and subcortical brain measures. For stroke patients, it could serve as a more sensitive marker of brain health than chronological age. In this study, we investigated whether there is a difference in brain age between stroke survivors and control participants matched on chronological age. We estimated brain age at 3 months after stroke, and then followed the longitudinal trajectory over three time-points: within 6 weeks (baseline), at 3 and at 12 months following their clinical event. We found that brain age in stroke participants was higher compared to controls, with the mean difference between the groups varying between 3.9 and 8.7 years depending on the brain measure used for prediction. This difference in brain age was observed at 6 weeks after stroke and maintained at 3 and 12 months after stroke. The presence of group differences already at baseline suggests that stroke might be an ultimate manifestation of gradual cerebrovascular burden accumulation and brain degeneration. Brain age prediction, therefore, has the potential to be a useful biomarker for quantifying stroke risk., (Copyright © 2019 Egorova, Liem, Hachinski and Brodtmann.)

Age-related differences in white matter (WM) microstructure have been linked to lower performance in tasks of processing speed in healthy older individuals. However, only few studies have examined this link in a longitudinal setting. These investigations have been limited to the correlation of simultaneous changes in WM microstructure and processing speed. Still little is known about the nature of age-related changes in WM microstructure, i.e., regionally distinct vs. global changes. In the present study, we addressed these open questions by exploring whether previous changes in WM microstructure were related to subsequent changes in processing speed: (a) 1 year later; or (b) 2 years later. Furthermore, we investigated whether age-related changes in WM microstructure were regionally specific or global. We used data from four occasions (covering 4 years) of the Longitudinal Healthy Aging Brain (LHAB) database project ( N = 232; age range at baseline = 64-86). As a measure of WM microstructure, we used mean fractional anisotropy (FA) in 10 major WM tracts averaged across hemispheres. Processing speed was measured with four cognitive tasks. Statistical analyses were conducted with bivariate latent change score (LCS) models. We found, for the first time, evidence for lagged couplings between preceding changes in FA and subsequent changes in processing speed 2 years, but not 1 year later in some of the WM tracts (anterior thalamic radiation, superior longitudinal fasciculus). Our results supported the notion that FA changes were different between regional WM tracts rather than globally shared, with some tracts showing mean declines in FA, and others remaining relatively stable across 4 years., (Copyright © 2019 Oschwald, Mérillat, Liem, Röcke, Martin and Jäncke.)

The question whether body height is related to different brain size measures has recently gained renewed interest as some studies have reported that body height correlates with intelligence and several brain size measures. In this study, we re-evaluated this question by examining the relationship between body height and different brain size measures including intracranial volume, total brain volume, total cortical surface area, total cortical volume, volume of normal-appearing white matter, white matter hyperintensity, cortical surface area, cortical thickness, subcortical grey matter volume, cerebellar cortex and cerebellar white matter in a relatively large sample (n = 216) of physically and cognitively healthy elderly subjects (mean age 71 years, age range 65-85 years). We identified small correlations (r = .11-.19) between body height and seven out of 10 brain metrics (total brain volume, cortical surface area, cortical volume, subcortical volume, normal-appearing white matter volume and cerebellar grey as well as white matter volumes) when controlling for sex and age. Based on these small relationships between body height and various brain size measures, we discuss the possible reasons and theoretical problems for these small relationships., (© 2019 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Normal aging is accompanied by an interindividually variable decline in cognitive abilities and brain structure. This variability, in combination with methodical differences and differences in sample characteristics across studies, pose a major challenge for generalizability of results from different studies. Therefore, the current study aimed at cross-validating age-related differences in cognitive abilities and brain structure (measured using cortical thickness [CT]) in two large independent samples, each consisting of 228 healthy older adults aged between 65 and 85 years: the Longitudinal Healthy Aging Brain (LHAB) database (University of Zurich, Switzerland) and the 1000BRAINS (Research Centre Jülich, Germany). Participants from LHAB showed significantly higher education, physical well-being, and cognitive abilities (processing speed, concept shifting, reasoning, semantic verbal fluency, and vocabulary). In contrast, CT values were larger for participants of 1000BRAINS. Though, both samples showed highly similar age-related differences in both, cognitive abilities and CT. These effects were in accordance with functional aging theories, for example, posterior to anterior shift in aging as was shown for the default mode network. Thus, the current two-study approach provides evidence that independently on heterogeneous metrics of brain structure or cognition across studies, age-related effects on cognitive ability and brain structure can be generalized over different samples, assuming the same methodology is used., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

In this study, we examine the relationship between total brain volume (BV) and the volumes of several main brain compartmental (BC) measures (cortical thickness, cortical surface area, corpus callosum, cortical gray matter, normal appearing cerebral white matter (NAWM), amygdala, accumbens, caudate, hippocampus, putamen, pallidum, thalamus, cerebellar gray matter, and cerebellar WM) of physically and cognitively healthy elderly individuals (mean age: 71 years, age range: 65-85 years). The statistical analysis uncovered extremely different relationships between total BV and the aforementioned BC metrics. These relationships ranged from extremely strong (BV explaining 85% of the variability of cerebral WM volume) to a very small relationship (for the caudate volume and the cortical thickness). In addition, cerebral WM and the accumbens volumes scaled out of proportion with BV, whereas most other BC measures scaled less than proportional to BV. Thus, larger brains exhibit relatively larger cerebral NAWM and accumbens volumes than do smaller brains. Cortical gray matter (and most other BC measures), on the other hand, relatively decreases as BV increases, resulting in relatively small cortical gray matter volumes (and relatively small BC measures) for large brains. These relationships are discussed within the context of general allometric scaling principles for the human brain. In addition, possible methodological consequences of analyzing anatomical data on the basis of MRI measurements are also discussed.

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.

Broca's region can be subdivided into its constituent areas 44 and 45 based on established differences in connectivity to superior temporal and inferior parietal regions. The current study builds on our previous work manually parcellating Broca's area on the individual-level by applying these anatomical criteria to functional connectivity data. Here we present an automated observer-independent and anatomy-informed parcellation pipeline with comparable precision to the manual labels at the individual-level. The method first extracts individualized connectivity templates of areas 44 and 45 by assigning to each surface vertex within the ventrolateral frontal cortex the partial correlation value of its functional connectivity to group-level templates of areas 44 and 45, accounting for other template connectivity patterns. To account for cross-subject variability in connectivity, the partial correlation procedure is then repeated using individual-level network templates, including individual-level connectivity from areas 44 and 45. Each node is finally labeled as area 44, 45, or neither, using a winner-take-all approach. The method also incorporates prior knowledge of anatomical location by weighting the results using spatial probability maps. The resulting area labels show a high degree of spatial overlap with the gold-standard manual labels, and group-average area maps are consistent with cytoarchitectonic probability maps of areas 44 and 45. To facilitate reproducibility and to demonstrate that the method can be applied to resting-state fMRI datasets with varying acquisition and preprocessing parameters, the labeling procedure is applied to two open-source datasets from the Human Connectome Project and the Nathan Kline Institute Rockland Sample. While the current study focuses on Broca's region, the method is adaptable to parcellate other cortical regions with distinct connectivity profiles., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

We examined whether it is possible to identify individual subjects on the basis of brain anatomical features. For this, we analyzed a dataset comprising 191 subjects who were scanned three times over a period of two years. Based on FreeSurfer routines, we generated three datasets covering 148 anatomical regions (cortical thickness, area, volume). These three datasets were also combined to a dataset containing all of these three measures. In addition, we used a dataset comprising 11 composite anatomical measures for which we used larger brain regions (11LBR). These datasets were subjected to a linear discriminant analysis (LDA) and a weighted K-nearest neighbors approach (WKNN) to identify single subjects. For this, we randomly chose a data subset (training set) with which we calculated the individual identification. The obtained results were applied to the remaining sample (test data). In general, we obtained excellent identification results (reasonably good results were obtained for 11LBR using WKNN). Using different data manipulation techniques (adding white Gaussian noise to the test data and changing sample sizes) still revealed very good identification results, particularly for the LDA technique. Interestingly, using the small 11LBR dataset also revealed very good results indicating that the human brain is highly individual.

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain-imaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N=2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N=475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders., (Copyright © 2017 Elsevier Inc. All rights reserved.)