Part IV. Neuroimaging in dementing disorders

A novel voxel-based method to estimate cortical sulci width and its application to compare patients with Alzheimer's disease to controls

A voxel-based method for measuring sulcal width was developed, validated and applied to a database. This method (EDT-based LM) employs the 3D Euclidean Distance Transform (EDT) of the pial surface and a Local Maxima labeling algorithm. A computational phantom was designed to test method performance; results revealed the method's inaccuracy δ, to range between 0.1 and 0.5 voxels, for a width that varied between 1 and 7 voxels. Two morphological descriptors were computed to characterize each defined sulcus: mean sulcal width (MSW) and mean absolute deviation (MAD). The former is the average width for all available width measurements within the sulcus, and the latter is the deviation of these measurements. The EDT-based LM method was applied to the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution Magnetic Resonance (MR) images of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. AD causes significant gray matter loss; hence, some sulci were expected to broaden. Methodological results concurred with this hypothesis. After a Wilcoxon test, MSW was grater in the case of all sulci pertaining to AD patients, (p < 0.05, FDR corrected), whereas MAD showed significant differences in 8 sulci (p < 0.05, FDR corrected). This work presents a novel voxel-based method for measuring sulcal width and extracting descriptors to characterize and compare the sulci within and across subjects.

Mapping the effect of the apolipoprotein E genotype on 4-year atrophy rates in an Alzheimer disease-related brain network

PURPOSE

To determine the effect of the apolipoprotein E (APOE) genotype on atrophy rates of specific brain gray matter regions hypothesized to be key components of cognitive networks disrupted in Alzheimer disease.

MATERIALS AND METHODS

The Alzheimer's Disease Neuroimaging Initiative (ADNI) was approved by the institutional review boards of all participating sites. All subjects and their legal representatives gave written informed consent prior to data collection. The authors analyzed data from 237 subjects (mean age, 79.9 years; 40% female) with mild cognitive impairment (MCI) in the ADNI database and assessed the effect of the APOE ε4 and ε2 alleles on regional brain atrophy rates over a 12-48-month period. Brain regions were selected a priori: 15 experimental and five control regions were included. Regional atrophy rates were derived by using a fully automated algorithm applied to T1-weighted magnetic resonance (MR) imaging data. Analysis consisted of mixed-effects linear regression with repeated measures; results were adjusted for multiple testing with Bonferroni correction.

RESULTS

Thirteen of 15 experimental regions showed a significant effect of ε4 for higher atrophy rates (P < .001 for all). Cohen d values ranged from 0.26 to 0.42, with the largest effects seen in the amygdalae and hippocampi. The transverse temporal cortex showed a trend (P = .02, but did not survive Bonferroni correction) for a protective effect (Cohen d value = 0.15) of ε2. No control region showed an APOE effect.

CONCLUSION

The APOE ε4 allele is associated with accelerated rates of atrophy in 13 distinct brain regions in limbic and neocortical areas. This suggests the possibility of a genotype-specific network of related brain regions that undergo faster atrophy in MCI and potentially contribute to cognitive decline. Online supplemental material is available for this article.

Annual research review: progress in using brain morphometry as a clinical tool for diagnosing psychiatric disorders

Brain morphometry in recent decades has increased our understanding of the neural bases of psychiatric disorders by localizing anatomical disturbances to specific nuclei and subnuclei of the brain. At least some of these disturbances precede the overt expression of clinical symptoms and possibly are endophenotypes that could be used to diagnose an individual accurately as having a specific psychiatric disorder. More accurate diagnoses could significantly reduce the emotional and financial burden of disease by aiding clinicians in implementing appropriate treatments earlier and in tailoring treatment to the individual needs. Several methods, especially those based on machine learning, have been proposed that use anatomical brain measures and gold-standard diagnoses of participants to learn decision rules that classify a person automatically as having one disorder rather than another. We review the general principles and procedures for machine learning, particularly as applied to diagnostic classification, and then review the procedures that have thus far attempted to diagnose psychiatric illnesses automatically using anatomical measures of the brain. We discuss the strengths and limitations of extant procedures and note that the sensitivity and specificity of these procedures in their most successful implementations have approximated 90%. Although these methods have not yet been applied within clinical settings, they provide strong evidence that individual patients can be diagnosed accurately using the spatial pattern of disturbances across the brain.

Morphometric analysis of gray matter volume in demented older adults: exploratory analysis of the cardiovascular health study brain MRI database

We tested the feasibility of a fully automated brain MRI voxel count technique--automated labeling pathway (ALP)--in a sample of 15 demented and 13 cognitively normal women (age 75-85 years) participating to the Cardiovascular Health Study (CHS). We hypothesized that ALP would replicate well-established findings of the anatomical correlates of dementia. In particular, we hypothesized that ALP volumetric measures would: (1) significantly differ between cognitively normal and demented women in those brain areas that are established markers for diagnosis of dementia (temporal and medial temporal lobes, hippocampus, amygdala and parahippocampus) but not in other brain areas (e.g., occipital lobe, visual cortex, motor cortex) and (2) correlate with visual ratings of brain disease which have been previously collected as part of the CHS. ALP required minimal operator intervention (input of brain images and verification of misalignments) and employed computer time of about 1 h per brain. ALP detected significant focal volumetric differences in the limbic system (p values between groups for hippocampus and parahippocampus: 0.002 and 0.005, respectively), temporal lobe (p < 0.0001) and caudate (p = 0.009), but not in other brain areas (e.g. occipital lobe, visual or motor cortex). Furthermore, ALP measures of medial temporal lobe atrophy strongly correlated with CHS visual ratings of ventricular enlargement (r(2) = 0.6, p = 0.002 for medial temporal lobe). In conclusion, ALP-detected focal brain atrophy was strongly associated with dementia. Because of its fully automated design, ALP technique is an ideal candidate to assess whether volumetric measures of specific areas can discriminate dementia better than currently available measures of global brain atrophy in large epidemiological studies.

Dynamics of gray matter loss in Alzheimer's disease

We detected and mapped a dynamically spreading wave of gray matter loss in the brains of patients with Alzheimer's disease (AD). The loss pattern was visualized in four dimensions as it spread over time from temporal and limbic cortices into frontal and occipital brain regions, sparing sensorimotor cortices. The shifting deficits were asymmetric (left hemisphere > right hemisphere) and correlated with progressively declining cognitive status (p < 0.0006). Novel brain mapping methods allowed us to visualize dynamic patterns of atrophy in 52 high-resolution magnetic resonance image scans of 12 patients with AD (age 68.4 +/- 1.9 years) and 14 elderly matched controls (age 71.4 +/- 0.9 years) scanned longitudinally (two scans; interscan interval 2.1 +/- 0.4 years). A cortical pattern matching technique encoded changes in brain shape and tissue distribution across subjects and time. Cortical atrophy occurred in a well defined sequence as the disease progressed, mirroring the sequence of neurofibrillary tangle accumulation observed in cross sections at autopsy. Advancing deficits were visualized as dynamic maps that change over time. Frontal regions, spared early in the disease, showed pervasive deficits later (>15% loss). The maps distinguished different phases of AD and differentiated AD from normal aging. Local gray matter loss rates (5.3 +/- 2.3% per year in AD v 0.9 +/- 0.9% per year in controls) were faster in the left hemisphere (p < 0.029) than the right. Transient barriers to disease progression appeared at limbic/frontal boundaries. This degenerative sequence, observed in vivo as it developed, provides the first quantitative, dynamic visualization of cortical atrophic rates in normal elderly populations and in those with dementia.