Relationship Between Regional Brain Volumes and Cognitive Performance in the Healthy Aging: An MRI Study Using Voxel-Based Morphometry

The presence of cognitive impairment is a frequent complaint among elderly individuals in the general population. This study aimed to investigate the relationship between aging-related regional gray matter (rGM) volume changes and cognitive performance in healthy elderly adults. Morphometric magnetic resonance imaging (MRI) measures were acquired in a community-based sample of 170 cognitively-preserved subjects (66 to 75 years). This sample was drawn from the "Sao Paulo Ageing and Health" study, an epidemiological study aimed at investigating the prevalence and risk factors for Alzheimer's disease in a low income region of the city of Sao Paulo. All subjects underwent cognitive testing using a cross-culturally battery validated by the Research Group on Dementia 10/66 as well as the SKT (applied on the day of MRI scanning). Blood genotyping was performed to determine the frequency of the three apolipoprotein E allele variants (APOE epsilon 2/epsilon 3/epsilon 4) in the sample. Voxelwise linear correlation analyses between rGM volumes and cognitive test scores were performed using voxel-based morphometry, including chronological age as covariate. There were significant direct correlations between worse overall cognitive performance and rGM reductions in the right orbitofrontal cortex and parahippocampal gyrus, and also between verbal fluency scores and bilateral parahippocampal gyral volume (p < 0.05, familywise-error corrected for multiple comparisons using small volume correction). When analyses were repeated adding the presence of the APOE epsilon 4 allele as confounding covariate or excluding a minority of APOE epsilon 2 carriers, all findings retained significance. These results indicate that rGM volumes are relevant biomarkers of cognitive deficits in healthy aging individuals, most notably involving temporolimbic regions and the orbitofrontal cortex.

Longitudinal brain volumetric changes during one year in non-elderly healthy adults: a voxel-based morphometry study

Previous cross-sectional magnetic resonance imaging (MRI) studies of healthy aging in young adults have indicated the presence of significant inverse correlations between age and gray matter volumes, although not homogeneously across all brain regions. However, such cross-sectional studies have important limitations and there is a scarcity of detailed longitudinal MRI studies with repeated measures obtained in the same individuals in order to investigate regional gray matter changes during short periods of time in non-elderly healthy adults. In the present study, 52 healthy young adults aged 18 to 50 years (27 males and 25 females) were followed with repeated MRI acquisitions over approximately 15 months. Gray matter volumes were compared between the two times using voxel-based morphometry, with the prediction that volume changes would be detectable in the frontal lobe, temporal neocortex and hippocampus. Voxel-wise analyses showed significant (P < 0.05, family-wise error corrected) relative volume reductions of gray matter in two small foci located in the right orbitofrontal cortex and left hippocampus. Separate comparisons for males and females showed bilateral gray matter relative reductions in the orbitofrontal cortex over time only in males. We conclude that, in non-elderly healthy adults, subtle gray matter volume alterations are detectable after short periods of time. This underscores the dynamic nature of gray matter changes in the brain during adult life, with regional volume reductions being detectable in brain regions that are relevant to cognitive and emotional processes.

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties.

Analysis of cone-beam ct dose in image-guided radiation therapy for brain and prostate cancer patients via gpu-based Monte Carlo simulations

La radioterapia guidata da immagini (IGRT), grazie alle ripetute verifiche della posizione del paziente e della localizzazione del volume bersaglio, si è recentemente affermata come nuovo paradigma nella radioterapia, avendo migliorato radicalmente l’accuratezza nella somministrazione di dose a scopo terapeutico. Una promettente tecnica nel campo dell’IGRT è rappresentata dalla tomografia computerizzata a fascio conico (CBCT). La CBCT a kilovoltaggio, consente di fornire un’accurata mappatura tridimensionale dell’anatomia del paziente, in fase di pianificazione del trattamento e a ogni frazione del medisimo. Tuttavia, la dose da imaging attribuibile alle ripetute scansioni è diventata, negli ultimi anni, oggetto di una crescente preoccupazione nel contesto clinico. Lo scopo di questo lavoro è di valutare quantitativamente la dose addizionale somministrata da CBCT a kilovoltaggio, con riferimento a tre tipici protocolli di scansione per Varian OnBoard Imaging Systems (OBI, Palo Alto, California). A questo scopo sono state condotte simulazioni con codici Monte Carlo per il calcolo della dose, utilizzando il pacchetto gCTD, sviluppato sull’architettura della scheda grafica. L’utilizzo della GPU per sistemi server di calcolo ha permesso di raggiungere alte efficienze computazionali, accelerando le simulazioni Monte Carlo fino a raggiungere tempi di calcolo di ~1 min per un caso tipico. Inizialmente sono state condotte misure sperimentali di dose su un fantoccio d’acqua. I parametri necessari per la modellazione della sorgente di raggi X nel codice gCTD sono stati ottenuti attraverso un processo di validazione del codice al fine di accordare i valori di dose simulati in acqua con le misure nel fantoccio. Lo studio si concentra su cinquanta pazienti sottoposti a cicli di radioterapia a intensità modulata (IMRT). Venticinque pazienti con tumore al cervello sono utilizzati per studiare la dose nel protocollo standard-dose head e venticinque pazienti con tumore alla prostata sono selezionati per studiare la dose nei protocolli pelvis e pelvis spotlight. La dose media a ogni organo è calcolata. La dose media al 2% dei voxels con i valori più alti di dose è inoltre computata per ogni organo, al fine di caratterizzare l’omogeneità spaziale della distribuzione.

Graph-based analysis of brain resting-state fMRI data in nocturnal frontal lobe epileptic patients

Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull'analisi dati di resting state - functional Magnetic Resonance Imaging (rs-fMRI) mediante l'utilizzo della graph theory, con lo scopo di valutare eventuali differenze in termini di connettività cerebrale funzionale tra un campione di pazienti affetti da Nocturnal Frontal Lobe Epilepsy (NFLE) ed uno di controlli sani. L'epilessia frontale notturna è una peculiare forma di epilessia caratterizzata da crisi che si verificano quasi esclusivamente durante il sonno notturno. Queste sono contraddistinte da comportamenti motori, prevalentemente distonici, spesso complessi, e talora a semiologia bizzarra. L'fMRI è una metodica di neuroimaging avanzata che permette di misurare indirettamente l'attività neuronale. Tutti i soggetti sono stati studiati in condizioni di resting-state, ossia di veglia rilassata. In particolare mi sono occupato di analizzare i dati fMRI con un approccio innovativo in campo clinico-neurologico, rappresentato dalla graph theory. I grafi sono definiti come strutture matematiche costituite da nodi e links, che trovano applicazione in molti campi di studio per la modellizzazione di strutture di diverso tipo. La costruzione di un grafo cerebrale per ogni partecipante allo studio ha rappresentato la parte centrale di questo lavoro. L'obiettivo è stato quello di definire le connessioni funzionali tra le diverse aree del cervello mediante l'utilizzo di un network. Il processo di modellizzazione ha permesso di valutare i grafi neurali mediante il calcolo di parametri topologici che ne caratterizzano struttura ed organizzazione. Le misure calcolate in questa analisi preliminare non hanno evidenziato differenze nelle proprietà globali tra i grafi dei pazienti e quelli dei controlli. Alterazioni locali sono state invece riscontrate nei pazienti, rispetto ai controlli, in aree della sostanza grigia profonda, del sistema limbico e delle regioni frontali, le quali rientrano tra quelle ipotizzate essere coinvolte nella fisiopatologia di questa peculiare forma di epilessia.

Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Developmental Brain Dysfunction: Revival and Expansion of Old Concepts Based on New Genetic Evidence

Neurodevelopmental disorders can be caused by many different genetic abnormalities that are individually rare but collectively common. Specific genetic causes, including certain copy number variants and single-gene mutations, are shared among disorders that are thought to be clinically distinct. This evidence of variability in the clinical manifestations of individual genetic variants and sharing of genetic causes among clinically distinct brain disorders is consistent with the concept of developmental brain dysfunction, a term we use to describe the abnormal brain function underlying a group of neurodevelopmental and neuropsychiatric disorders and to encompass a subset of various clinical diagnoses. Although many pathogenic genetic variants are currently thought to be variably penetrant, we hypothesise that when disorders encompassed by developmental brain dysfunction are considered as a group, the penetrance will approach 100%. The penetrance is also predicted to approach 100% when the phenotype being considered is a specific trait, such as intelligence or autistic-like social impairment, and the trait could be assessed using a continuous, quantitative measure to compare probands with non-carrier family members rather than a qualitative, dichotomous trait and comparing probands with the healthy population. Copyright 2013 Elsevier Ltd. All rights reserved.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

PCR-based diagnosis of Naegleria sp. infection in formalin-fixed and paraffin-embedded brain sections

We developed a real-time PCR which allowed the highly sensitive detection of Naegleria fowleri in histological brain tissue sections from experimentally infected mice. This genus-specific small-subunit (18S) rRNA gene-based PCR can complement conventional (immuno-) histology for the diagnosis of primary amoebic meningoencephalitis in paraffin-embedded brain necropsy specimens that had been fixed in formalin buffered with phosphate-buffered saline.

MRI-based brain volumetry in chronic whiplash patients: no evidence for traumatic brain injury

INTRODUCTION: Cognitive complaints, such as poor concentration and memory deficits, are frequent after whiplash injury and play an important role in disability. The origin of these complaints is discussed controversially. Some authors postulate brain lesions as a consequence of whiplash injuries. Potential diffuse axonal injury (DAI) with subsequent atrophy of the brain and ventricular expansion is of particular interest as focal brain lesions have not been documented so far in whiplash injury. OBJECTIVE: To investigate whether traumatic brain injury can be identified using a magnetic resonance (MR)-based quantitative analysis of normalized ventricle-brain ratios (VBR) in chronic whiplash patients with subjective cognitive impairment that cannot be objectively confirmed by neuropsychological testing. MATERIALS AND METHODS: MR examination was performed in 21 patients with whiplash injury and symptom persistence for 9 months on average and in 18 matched healthy controls. Conventional MR imaging (MRI) was used to assess the volumes of grey and white matter and of ventricles. The normalized VBR was calculated. RESULTS: The values of normalized VBR did not differ in whiplash patients when compared with that in healthy controls (F = 0.216, P = 0.645). CONCLUSIONS: This study does not support loss of brain tissue following whiplash injury as measured by VBR. On this basis, traumatic brain injury with subsequent DAI does not seem to be the underlying mechanism for persistent concentration and memory deficits that are subjectively reported but not objectively verifiable as neuropsychological deficits.

Statistical evaluation of time-dependent metabolite concentrations: estimation of post-mortem intervals based on in situ 1H-MRS of the brain

Knowledge of the time interval from death (post-mortem interval, PMI) has an enormous legal, criminological and psychological impact. Aiming to find an objective method for the determination of PMIs in forensic medicine, 1H-MR spectroscopy (1H-MRS) was used in a sheep head model to follow changes in brain metabolite concentrations after death. Following the characterization of newly observed metabolites (Ith et al., Magn. Reson. Med. 2002; 5: 915-920), the full set of acquired spectra was analyzed statistically to provide a quantitative estimation of PMIs with their respective confidence limits. In a first step, analytical mathematical functions are proposed to describe the time courses of 10 metabolites in the decomposing brain up to 3 weeks post-mortem. Subsequently, the inverted functions are used to predict PMIs based on the measured metabolite concentrations. Individual PMIs calculated from five different metabolites are then pooled, being weighted by their inverse variances. The predicted PMIs from all individual examinations in the sheep model are compared with known true times. In addition, four human cases with forensically estimated PMIs are compared with predictions based on single in situ MRS measurements. Interpretation of the individual sheep examinations gave a good correlation up to 250 h post-mortem, demonstrating that the predicted PMIs are consistent with the data used to generate the model. Comparison of the estimated PMIs with the forensically determined PMIs in the four human cases shows an adequate correlation. Current PMI estimations based on forensic methods typically suffer from uncertainties in the order of days to weeks without mathematically defined confidence information. In turn, a single 1H-MRS measurement of brain tissue in situ results in PMIs with defined and favorable confidence intervals in the range of hours, thus offering a quantitative and objective method for the determination of PMIs.

Interactive Curriculum Based on Models of Mind & Brain

What does it mean for curriculum to be interactive? It encourages student engagement and active participation in both individual and group work. It offers teachers a coherent set of materials to choose from that can enhance their classes. It is the product of on-going development and continuous improvement based on research and feedback from the field. This paper will introduce work in progress from the Center for Excellence in Education, Science, and Technology (CELEST), an NSF Science of Learning Center. Among its many goals, CELEST is developing a unique educational curriculum, an interactive curriculum based upon models of mind and brain. Teachers, administrators, and governments are naturally concerned with how students learn. Students are greatly concerned about how minds work, including how to learn. CELEST aims to introduce curricula that not only meet current U.S. standards in mathematics, science, and psychology but also influence plans to improve those standards. Software and support materials are in development and available at http://cns.bu.edu/celest/private/. Interested parties are invited to contact the author for access.