Cortex mapping reveals regionally specific patterns of genetic and disease-specific gray-matter deficits in twins discordant for schizophrenia

Voxel-based morphometry of grey matter densities in subjects at high risk of schizophrenia

The grey matter (GM) segments from T1 structural magnetic resonance (MR) images of the brain in subjects at high risk of schizophrenia (n=146) were compared with normal control subjects (n=36) and first episode schizophrenic subjects (n=34) using automated voxel-based morphometry (VBM). The subjects were recruited for the Edinburgh High Risk Study (EHRS) and regional brain volumes had previously been measured using a semi-automated volumetric region of interest (ROI) method of analysis. For the current report, the images were processed using a study specific template and statistically analysed using the SPM99 program. The small volume correction tool in SPM was also used to restrict the analyses to specific voxels. Reductions in the probability of grey matter (GM) density were seen bilaterally in the anterior cingulate, and as a trend in the left parahippocampal gyrus for the high-risk vs. control subjects. In contrast, first episode schizophrenia subjects had less GM than high-risk subjects in several frontal and temporal regions. These results are compatible with the findings of our previous volumetric ROI analysis.

The LONI Pipeline Processing Environment

The analysis of raw data in neuroimaging has become a computationally entrenched process with many intricate steps run on increasingly larger datasets. Many software packages exist that provide either complete analyses or specific steps in an analysis. These packages often possess diverse input and output requirements, utilize different file formats, run in particular environments, and have limited abilities with certain types of data. The combination of these packages to achieve more sensitive and accurate results has become a common tactic in brain mapping studies but requires much work to ensure valid interoperation between programs. The handling, organization, and storage of intermediate data can prove difficult as well. The LONI Pipeline Processing Environment is a simple, efficient, and distributed computing solution to these problems enabling software inclusion from different laboratories in different environments. It is used here to derive a T1-weighted MRI atlas of the human brain from 452 normal young adult subjects with fully automated processing. The LONI Pipeline Processing Environment's parallel processing efficiency using an integrated client/server dataflow model was 80.9% when running the atlas generation pipeline from a PC client (Acer TravelMate 340T) on 48 dedicated server processors (Silicon Graphics Inc. Origin 3000). The environment was 97.5% efficient when the same analysis was run on eight dedicated processors.

Structural and functional abnormalities of the amygdala in schizophrenia

Schizophrenia is characterized by delusions and hallucinations, which tend to respond to treatment with dopamine receptor blockers, and a loss of motivation and affect, which do not. Structural magnetic resonance imaging (sMRI) has convincingly demonstrated reduced volumes of the amygdala-hippocampal complex (AHC) and other limbic and paralimbic structures, on both manual tracing and automated analyses. The Edinburgh High-Risk Study (EHRS) of initially healthy adolescents with at least two affected relatives has found that AHC volumes are reduced pre-morbidly but not to schizophrenic levels, suggesting that further volume reductions may be associated with the onset of schizophrenia. AHC volumes appear to be genetically mediated in families with a dominant pattern of transmission, whereas prefrontal lobe and basal ganglia volumes are related to genetic liability to schizophrenia in the generality of high-risk subjects. Temporal lobe volumes may fall as psychotic symptoms develop, in the context of drug abuse and stress. Neuropsychological testing has also demonstrated pre-morbid impairments and symptom-related deterioration. More detailed analyses of the temporal lobe changes on sMRI and fronto-temporal dysconnectivity on fMRI are in progress. These findings are discussed with reference to other indications of pre-morbid developmental disturbance in our high-risk subjects, animal models of schizophrenia, and reliable findings from neuropathological, neuropsychological, and functional imaging studies of patients with schizophrenia.

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.

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.

A twin study of genetic contributions to hippocampal morphology in schizophrenia

Our goal was to establish whether altered hippocampal morphology represents a trait marker for genetic vulnerability in schizophrenia. We outlined the hippocampi on high-resolution MR images obtained from matched samples of control and discordant monozygotic and dizygotic co-twins (N = 40 pairs). Hippocampal measures were used in statistical tests specifically designed to identify disease-associated genetic and nongenetic influences on morphology. 3D surface average maps of the hippocampus were additionally compared in biological risk groups. Smaller hippocampal volumes were confirmed in schizophrenia. Dizygotic affected co-twins showed smaller left hippocampi compared to their healthy siblings. Disease-associated effects were not present between monozygotic discordant co-twins. Monozygotic, but not dizygotic, unaffected co-twins exhibited smaller left hippocampi compared to control twins, supporting genetic influences. Surface areas and posterior volumes similarly revealed schizophrenia and genetic liability effects. Results suggest that hippocampal volume reduction may be a trait marker for identifying individuals possessing a genetic predisposition for schizophrenia.

Genetic contributions to altered callosal morphology in schizophrenia

Patients with schizophrenia exhibit abnormalities in midsagittal corpus callosum area, shape, and/or displacement. Our goal was to confirm these findings and to establish the genetic and nongenetic contributions to altered callosal morphology in schizophrenia. Relationships between ventricular enlargements potentially contributing to callosal displacements were assessed as a secondary goal. High-resolution magnetic resonance images were obtained from co-twins of monozygotic and dizygotic pairs discordant for schizophrenia and healthy control twins (N = 40 pairs). Investigators blind to group status segmented the corpus callosum and ventricles in native brain volumes aligned using a rigid-body transformation with no scaling. Total and parcellated midsagittal callosal areas and measures indexing vertical displacements of the corpus callosum were used in statistical tests to identify schizophrenia and sex effects and to dissociate genetic and nongenetic influences on morphology. Anatomical mesh modeling methods provided group average and surface variability maps of the callosum. Callosal areas did not differ between groups defined by sex or biological risk. Vertical displacements of the callosum, pronounced in male patients, were confirmed in schizophrenia and observed between dizygotic, but not monozygotic co-twins discordant for schizophrenia. Like their affected twins, however, unaffected monozygotic co-twins of the schizophrenia probands exhibited significant callosal displacements. Lateral and third ventricle enlargements were related to callosal displacements. Results clearly support that genetic rather than disease-specific or shared environmental influences contribute to altered callosal morphology in schizophrenia. An upward bowing of the callosum may thus provide an easily identifiable neuroanatomic marker to screen individuals possessing a biological vulnerability for schizophrenia.

Mapping genetic influences on human brain structure

Recent advances in brain imaging and genetics have empowered the mapping of genetic and environmental influences on the human brain. These techniques shed light on the 'nature/nurture' debate, revealing how genes determine individual differences in intelligence quotient (IQ) or risk for disease. They visualize which aspects of brain structure and function are heritable, and to what degree, linking these features with behavioral or cognitive traits or disease phenotypes. In genetically transmitted disorders such as schizophrenia, patterns of brain structure can be associated with increased disease liability, and sites can be mapped where non-genetic triggers may initiate disease. We recently developed a large-scale computational brain atlas, including data components from the Finnish Twin registry, to store information on individual variations in brain structure and their heritability. Algorithms from random field theory, anatomical modeling, and population genetics were combined to detect a genetic continuum in which brain structure is heavily genetically determined in some areas but not others. These algorithmic advances motivate studies of disease in which the normative atlas acts as a quantitative reference for the heritability of structural differences and deficits in patient populations. The resulting genetic brain maps isolate biological markers for inherited traits and disease susceptibility, which may serve as targets for genetic linkage and association studies. Computational methods from brain imaging and genetics can be fruitfully merged, to shed light on the inheritance of personality differences and behavioral traits, and the genetic transmission of diseases that affect the human brain.

IMAGING THE BRAIN AS SCHIZOPHRENIA DEVELOPS: DYNAMIC & GENETIC BRAIN MAPS

Schizophrenia is a chronic, debilitating psychiatric disorder that affects 0.2-2% of the population worldwide. Often striking without warning in the late teens or early twenties, its symptoms include auditory and visual hallucinations, psychotic outbreaks, bizarre or disordered thinking, depression and social withdrawal. To combat the disease, new antipsychotic drugs are emerging; these atypical neuroleptics target dopamine and serotonin pathways in the brain, offering increased therapeutic efficacy with fewer side effects. Despite their moderate success in controlling some patients' symptoms, little is known about the causes of schizophrenia, and what triggers the disease. Its peculiar age of onset raises key questions: What physical changes occur in the brain as a patient develops schizophrenia? Do these deficits spread in the brain, and can they be opposed? How do they relate to psychotic symptoms? As risk for the disease is genetically transmitted, do a patient's relatives exhibit similar brain changes? Recent advances in brain imaging and genetics provide exciting insight on these questions. Neuroimaging can now chart the emergence and progression of deficits in the brain, providing an exceptionally sharp scalpel to dissect the effects of genetic risk, environmental triggers, and susceptibility genes. Visualizing the dynamics of the disease, these techniques also offer new strategies to evaluate drugs that combat the unrelenting symptoms of schizophrenia.