Michael S. Hong

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 Alzheimers 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.

Structural Abnormalities in the Brains of Human Subjects Who Use Methamphetamine

We visualize, for the first time, the profile of structural deficits in the human brain associated with chronic methamphetamine (MA) abuse. Studies of human subjects who have used MA chronically have revealed deficits in dopaminergic and serotonergic systems and cerebral metabolic abnormalities. Using magnetic resonance imaging (MRI) and new computational brain-mapping techniques, we determined the pattern of structural brain alterations associated with chronic MA abuse in human subjects and related these deficits to cognitive impairment. We used high-resolution MRI and surface-based computational image analyses to map regional abnormalities in the cortex, hippocampus, white matter, and ventricles in 22 human subjects who used MA and 21 age-matched, healthy controls. Cortical maps revealed severe gray-matter deficits in the cingulate, limbic, and paralimbic cortices of MA abusers (averaging 11.3% below control; p < 0.05). On average, MA abusers had 7.8% smaller hippocampal volumes than control subjects (p < 0.01; left, p = 0.01; right, p < 0.05) and significant white-matter hypertrophy (7.0%; p < 0.01). Hippocampal deficits were mapped and correlated with memory performance on a word-recall test (p < 0.05). MRI-based maps suggest that chronic methamphetamine abuse causes a selective pattern of cerebral deterioration that contributes to impaired memory performance. MA may selectively damage the medial temporal lobe and, consistent with metabolic studies, the cingulate-limbic cortex, inducing neuroadaptation, neuropil reduction, or cell death. Prominent white-matter hypertrophy may result from altered myelination and adaptive glial changes, including gliosis secondary to neuronal damage. These brain substrates may help account for the symptoms of MA abuse, providing therapeutic targets for drug-induced brain injury.

Mapping hippocampal and ventricular change in Alzheimer disease

We developed an anatomical mapping technique to detect hippocampal and ventricular changes in Alzheimer disease (AD). The resulting maps are sensitive to longitudinal changes in brain structure as the disease progresses. An anatomical surface modeling approach was combined with surface-based statistics to visualize the region and rate of atrophy in serial MRI scans and isolate where these changes link with cognitive decline. Sixty-two [corrected] high-resolution MRI scans were acquired from 12 AD patients (mean [corrected] age +/- SE at first scan: 68.7 +/- 1.7 [corrected] years) and 14 matched controls (age: 71.4 +/- 0.9 years) [corrected] each scanned twice (1.9 +/- 0.2 [corrected] years apart, when all subjects are pooled [corrected] 3D parametric mesh models of the hippocampus and temporal horns were created in sequential scans and averaged across subjects to identify systematic patterns of atrophy. As an index of radial atrophy, 3D distance fields were generated relating each anatomical surface point to a medial curve threading down the medial axis of each structure. Hippocampal atrophic rates and ventricular expansion were assessed statistically using surface-based permutation testing and were faster in AD than in controls. Using color-coded maps and video sequences, these changes were visualized as they progressed anatomically over time. Additional maps localized regions where atrophic changes linked with cognitive decline. Temporal horn expansion maps were more sensitive to AD progression than maps of hippocampal atrophy, but both maps correlated with clinical deterioration. These quantitative, dynamic visualizations of hippocampal atrophy and ventricular expansion rates in aging and AD may provide a promising measure to track AD progression in drug trials.

Cerebral Metabolic Dysfunction and Impaired Vigilance in Recently Abstinent Methamphetamine Abusers

BACKGROUND Methamphetamine (MA) abusers have cognitive deficits, abnormal metabolic activity and structural deficits in limbic and paralimbic cortices, and reduced hippocampal volume. The links between cognitive impairment and these cerebral abnormalities are not established. METHODS We assessed cerebral glucose metabolism with [F-18]fluorodeoxyglucose positron emission tomography in 17 abstinent (4 to 7 days) methamphetamine users and 16 control subjects performing an auditory vigilance task and obtained structural magnetic resonance brain scans. Regional brain radioactivity served as a marker for relative glucose metabolism. Error rates on the task were related to regional radioactivity and hippocampal morphology. RESULTS Methamphetamine users had higher error rates than control subjects on the vigilance task. The groups showed different relationships between error rates and relative activity in the anterior and middle cingulate gyrus and the insula. Whereas the MA user group showed negative correlations involving these regions, the control group showed positive correlations involving the cingulate cortex. Across groups, hippocampal metabolic and structural measures were negatively correlated with error rates. CONCLUSIONS Dysfunction in the cingulate and insular cortices of recently abstinent MA abusers contribute to impaired vigilance and other cognitive functions requiring sustained attention. Hippocampal integrity predicts task performance in methamphetamine users as well as control subjects.

Time-Lapse Mapping of Cortical Changes in Schizophrenia with Different Treatments

Using time-lapse maps, we visualized the dynamics of schizophrenia progression, revealing spreading cortical changes that depend on the type of antipsychotic treatment. Dynamic, 4-dimensional models of disease progression were created from 4 repeated high-resolution brain magnetic resonance imaging scans of 36 first-episode schizophrenia patients (30 men/6 women; mean age: 24.2 +/- 5.1 SD years) randomized to haloperidol (HAL) (n = 15) or olanzapine (OLZ) treatment (n = 21), imaged at baseline, 3, 6, and 12 months (144 scans). Based on surface-based cortical models and point-by-point measures of gray matter volume, we generated time-lapse maps for each treatment. Disease trajectories differed for atypical versus typical neuroleptic drugs. A rapidly advancing parietal-to-frontal deficit trajectory, in HAL-treated patients, mirrored normal cortical maturation but greatly intensified. The disease trajectory advanced even after symptom normalization, involving the frontal cortex within 12 months with typical drug treatment. Areas with fastest tissue loss shifted anteriorly in the first year of psychosis. This trajectory was not seen with OLZ. Whether this association reflects either reduced neurotoxicity or neuroprotection cannot be addressed with neuroimaging; changes may relate to glial rather than neural components. These maps revise current models of schizophrenia progression; due to power limitations, the findings require confirmation in a sample large enough to model group x time interactions.

Corrigendum to “Mapping hippocampal and ventricular change in Alzheimer disease” [NeuroImage 22 (2004) 1754–1766]

We recently noticed an error in the demographic data in this article. The validity of the findings and the conclusions of the paper is not affected. However, there is an error in the reported sample size and in the means and standard deviations of the subjects’ ages and MMSE scores. We would like to correct this error, which came to light when we were re-analyzing the data for a meta-analysis. The error occurred because an older version of a spreadsheet was incorrectly used when reporting the sample composition. Instead of examining 12 Alzheimer’s disease patients and 14 healthy elderly controls, we in fact examined 17 Alzheimer’s disease patients and 14 healthy elderly controls. All maps and morphometric data reported in the paper are correct, except that the sample size was in fact slightly higher than that originally reported, and the maps computed in the paper were based on the larger sample (which included five more subjects in the Alzheimer’s disease group). All of the maps and figures in the paper are correct, and the conclusions of the paper are unchanged. We apologize for this error, which falls under the sole responsibility of the first author. The corrected demographic information appears below.

Dynamic Mapping of Alzheimer’s Disease

Neuroimaging strategies to track Alzheimer’s disease are greatly accelerating our understanding of the disease. How early can we detect disease-related brain changes? How do these changes progress anatomically? Do drugs slow down the physical spread of the disease? Brain imaging now provides answers to some of these important questions. With recent innovations in magnetic resonance imaging (MRI) and brain image analysis, Alzheimer’s disease can be mapped dynamically as it spreads in the living brain (Reiman et al. 2001; Fox et al. 2001; Janke et al. 2001; Thompson et al. 2003a). Drug and gene effects on the disease process can be detected, both in patients and in family members at increased genetic risk. We show how these brain mapping tools help explore the dynamic processes of aging and dementia, revealing factors that affect them. As an illustrative example, we report the mapping of a dynamically spreading wave of gray matter loss in the brains of Alzheimer’s patients scanned repeatedly with MRI. The loss pattern is visualized, in 3D, as it spreads from temporal cortices into frontal and cingulate brain regions. Deficit patterns are resolved with a novel cortical pattern matching strategy (CPM). A dynamic mapping technique produces color-coded image sequences that reveal the disease spreading in the human cortex over a period of several years. The trajectory of cortical deficits, observed here in vivo with MRI, corresponded closely to the spread of the underlying pathology (as defined by the well-known Braak stages of neurofibrillary tangle and beta-amyloid accumulation). The magnitude of these deficits was also tightly linked with cognitive decline. In initial studies, these maps detected disease effects more sensitively than conventional cortical anatomic volume measures. By storing these dynamic brain maps in a growing, population-based digital atlas (N>1000 subjects), clinical imaging data can be analyzed on a large scale, adjusting for effects of age, sex, genotype, and disease subtypes. These maps chart the dynamic progress of Alzheimer’s disease and reveal a changing pattern of cortical deficits. We are now using them to detect where deficit patterns are modified by drug treatment and known risk genotypes.