Evaluation of Cerebral Lateral Ventricular Enlargement Derived from Magnetic Resonance Imaging: A Candidate Biomarker of Alzheimer Disease Progression in Vivo

Alzheimer disease (AD) is the most common form of dementia and has grievous mortality rates. Measuring brain volumes from structural magnetic resonance images (MRI) may be useful for illuminating disease progression. The goal of this thesis was to (1) help refine a novel technique used to segment the lateral cerebral ventricles from MRI, (2) validate this tool, and determine group-wise differences between normal elderly controls (NEC) and subjects with mild cognitive impairment (MCI) and AD and (3) determine the number of subjects necessary to detect a 20 percent change from the natural history of ventricular enlargement with respect to genotype. Three dimensional Ti-weighted MRI and cognitive measures were acquired from 504 subjects (NEC n = 152, MCI n = 247 and AD n = 105) participating in the multi-centre Alzheimer\u27s Disease Neuroimaging Initiative. Cerebral ventricular volume was quantified at baseline and after six months. For secondary analyses, all groups were dichotomized for Apolipoprotein E genotype based on the presence of an e4 polymorphism. The AD group had greater ventricular enlargement compared to both subjects with MCI (P = 0.0004) and NEC (P \u3c 0.0001), and subjects with MCI had a greater rate of ventricular enlargement compared to NEC (P =0.0001). MCI subjects that progressed to clinical AD after six months had greater ventricular enlargement than stable MCI subjects (P = 0.0270). Ventricular enlargement was different between apolipoprotein E genotypes within the AD group (P = 0.010). The number of subjects required to demonstrate a 20% change in ventricular enlargement (AD: N=342, MCI: N=1180) was substantially lower than that required to demonstrate a 20% change in cognitive scores (MMSE) (AD: N=7056, MCI: N=7712). Therefore, ventricular enlargement represents a feasible short-term marker of disease progression in subjects with MCI and subjects with AD for multi-centre studie

Texture‐based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population

Brain atrophy and cortical thinning are typically observed in people with Alzheimer's disease (AD) and, to a lesser extent, in those with mild cognitive impairment. In asymptomatic middle‐aged apolipoprotein ε4 (ΑPOE4) carriers, who are at higher risk of future AD, study reports are discordant with limited evidence of brain structural differences between carriers and non‐carriers of the ε4 allele. Alternative imaging markers with higher sensitivity at the presymptomatic stage, ideally quantified using typically acquired structural MRI scans, would thus be of great benefit for the detection of early disease, disease monitoring and subject stratification. In the present cross‐sectional study, we investigated textural properties of T1‐weighted 3T MRI scans in relation to APOE4 genotype, age and sex. We pooled together data from the PREVENT‐Dementia and ALFA studies focused on midlife healthy populations with dementia risk factors (analysable cohort: 1585 participants; mean age 56.2 ± 7.4 years). Voxel‐based and texture (examined features: contrast, entropy, energy, homogeneity) based morphometry was used to identify areas of volumetric and textural differences between APOE4 carriers and non‐carriers. Textural maps were generated and were subsequently harmonised using voxel‐wise COMBAT. For all analyses, APOE4, sex, age and years of education were used as model predictors. Interactions between APOE4 and age were further examined. There were no group differences in regional brain volume or texture based on APOE4 carriership or when age × APOE4 interactions were examined. Older people tended to have a less homogeneous textural profile in grey and white matter and a more homogeneous profile in the ventricles. A more heterogeneous textural profile was observed for females in areas such as the ventricles, frontal and parietal lobes and for males in the brainstem, cerebellum, precuneus and cingulate. Overall, we have shown the absence of volumetric and textural differences between APOE4 carriers and non‐carriers at midlife and have established associations of textural features with ageing and sex

Magnetic resonance imaging In Alzheimer’s disease, mild cognitive impairment and normal aging : Multi-template tensor-based morphometry and visual rating

Alzheimer's disease (AD) is the most common neurodegenerative disease preceded by a stage of mild cognitive impairment (MCI). The structural brain changes in AD can be detected more than 20 years before symptoms appear. If we are to reveal early brain changes in AD process, it is important to develop new diagnostic methods. Magnetic resonance imaging (MRI) is an imaging technique used in the diagnosis and monitoring of neurodegenerative diseases. Magnetic resonance imaging can detect the typical signs of brain atrophy of degenerative diseases, but similar changes can also be seen in normal aging. Visual rating methods (VRM) have been developed for visual evaluation of atrophy in dementia. A computer-based tensor-based morphometry (TBM) analysis is capable of assessing the brain volume changes typically encountered in AD. This study compared the VRM and TBM analysis in MCI and AD subjects by cross-sectional and longitudinal examination. The working hypothesis was that TBM analysis would be better than the visual methods in detecting atrophy in the brain. TBM was also used to analyze volume changes in the deep gray matter (DGM). Possible associations between TBM changes and neuropsychological tests performances were examined. This working hypothesis was that the structural DGM changes would be associated with impairments in cognitive functions. In the cross-sectional study, TBM distinguished the MCI from controls more sensitively than VRM, but the methods were equally effective in differentiating AD from MCI and controls. In the longitudinal study, both methods were equally good in the evaluation of atrophy in MCI, if the groups were sufficiently large and the disease progressed to AD. Volume changes were found in DGM structures, and the atrophy of DGM structures was related to cognitive impairment in AD. Based on these results, a TBM analysis is more sensitive in detecting brain changes in early AD as compared to VRM. In addition, the study produced information about the involvement of the deep gray matter in cognitive impairment in AD.Magneettikuvaus Alzheimerin taudissa, lievässä muistihäiriössä ja normaalissa ikääntymisessä: Tensoripohjainen muotoanalyysi ja visuaalinen arviointimenetelmä Alzheimerin tauti (AT) on yleisin dementoiva sairaus, jota edeltää yleensä lievä muistitoimintojen heikentyminen. AT:n aivomuutoksia voidaan todeta yli 20 vuotta ennen sairastumista. Jotta vielä varhaisempia AT:n aivomuutoksia voidaan todeta, on tärkeää kehittää uusia diagnostisia menetelmiä. Magneettikuvausta (MK) käytetään rappeuttavien aivosairauksien diagnostiikassa ja seurannassa. MK:lla voidaan havaita aivorappeumasairauksille tyypillistä kutistumista, mutta samanlaisia muutoksia voi esiintyä myös normaalissa ikääntymisessä. Aivorappeuman arviointiin on kehitetty silmämääräisiä arviointimenetelmiä. Tietokoneperusteinen tensoripohjainen muotoanalyysi (TPM) laskee esimerkiksi AT:lle tyypillisiä aivojen tilavuusmuutoksia. Tämä tutkimus vertaili silmämääräisiä arvioitimenetelmiä ja TPM:ä lievässä muistitoimintojen heikentymisessä ja AT:ssa poikittais- ja pitkittäistutkimuksella. TPM:n oletettiin olevan silmämääräisiä menetelmiä parempi tunnistamaan aivojen kutistumismuutoksia. Lisäksi TPM:llä tutkittiin AT:iin liittyviä aivojen syvän harmaan aiheen muutoksia, joita verrattiin neuropsykologisten testien tuloksiin. Syvän harmaan aineen kutistumisen oletettiin olevan yhteydessä tietojenkäsittelyn heikentymiseen. Tulosten perustella TPM tunnisti AT:iin liittyviä aivomuutoksia silmämääräistä menetelmää paremmin jo lievän muistitoimintojen heikentymisen vaiheessa. AT:iin liittyviä aivomuutoksia löytyi myös aivojen syvästä harmaasta aineesta ja ne olivat osittain yhteydessä neuropsykologisten testien tuloksiin. Tutkimuksen perusteella TPM voi parantaa AT:n varhaisdiagnostiikkaa verrattuna silmämääräisiin arviointimenetelmiin. Tutkimus antoi myös tietoa aivojen syvän harmaan aineen osallisuudesta ihmisen tietojenkäsittelyyn

Normative data for subcortical regional volumes over the lifetime of the adult human brain

Normative data for volumetric estimates of brain structures are necessary to adequately assess brain volume alterations in individuals with suspected neurological or psychiatric conditions. Although many studies have described age and sex effects in healthy individuals for brain morphometry assessed via magnetic resonance imaging, proper normative values allowing to quantify potential brain abnormalities are needed. We developed norms for volumetric estimates of subcortical brain regions based on cross-sectional magnetic resonance scans from 2790 healthy individuals aged 18 to 94 years using 23 samples provided by 21 independent research groups. The segmentation was conducted using FreeSurfer, a widely used and freely available automated segmentation software. Models predicting subcortical regional volumes of each hemisphere were produced including age, sex, estimated total intracranial volume (eTIV), scanner manufacturer, magnetic field strength, and interactions as predictors. The mean explained variance by the models was 48%. For most regions, age, sex and eTIV predicted most of the explained variance while manufacturer, magnetic field strength and interactions predicted a limited amount. Estimates of the expected volumes of an individual based on its characteristics and the scanner characteristics can be obtained using derived formulas. For a new individual, significance test for volume abnormality, effect size and estimated percentage of the normative population with a smaller volume can be obtained. Normative values were validated in independent samples of healthy adults and in adults with Alzheimer's disease and schizophrenia

BrainPrint: A discriminative characterization of brain morphology

We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765

BrainPrint: A discriminative characterization of brain morphology

We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765

Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

One physical sign of a person who has Alzheimer's is the diminution of the area of the hippocampus and ventricles. A good quality magnetic resonance imaging (MRI) will provide a high-quality image so that the doctor will quickly analyze the abnormalities of the hippocampus and ventricle area. However, for low-quality MRI, this is difficult to do. This condition will be a significant problem for some regions in developing countries including Indonesia, where many hospitals have only low-quality MRI, and many hospitals do not have them at all. The primary purpose of this research is to develop simple tools to analyze morphological characteristics in Alzheimer's patients. In this paper, we focus only on coronal slice analysis. We will use watershed method segmentation, because of this method able to segment the boundaries automatically, so that parts of the hippocampus and ventricles can be identified in an MRI image. Analysis of morphological characteristics is also classified by age and gender. Then by referring to the value of the clinical dementia rating (CDR), the process of identifying between images with Alzheimer's disease (AD) and healthy models is done based on the morphological analysis that has been done. The results show this method has a better performance compared to the previously work