Brain mapping as a tool to study neurodegeneration

A comprehensive investigation of the associations between tensor-based morphometry indices and executive functions, memory, language, and visuospatial abilities in patients in the Alzheimer's disease continuum

OBJECTIVES

Based on the literature, tensor-based morphometry (TBM) parameters were related to neurocognitive functions such as memory, learning, language ability, and executive functions. The present study aims to evaluate the associations between TBM indices with executive functions, memory, language, and visuospatial abilities and the value of TBM in the clinical diagnosis of Alzheimer's disease (AD) among individuals with Alzheimer's disease continuum and mild cognitive impairment (MCI) from Alzheimer's Disease Neuroimaging Initiative (ADNI).

METHODS

The authors used ADNI-memory (ADNI-MEM), ADNI-executive functions (ADNI-EF), ADNI-language (ADNI-LAN), and ADNI-visuospatial (ADNI-VS) composite scores. TBM parameters, including measure 1, which represents average within a statistically defined region-of-interest inside the temporal lobes and measure 2 which indicates average within an anatomically defined region-of-interest including bilateral temporal lobes were utilized in the current study. Statistical analysis was performed using IBM SPSS Statistics version 26, and Pearson's correlation, Bonferroni's correction, and multiple linear regression were utilized for data analysis. P <0.05 was considered statistically significant.

RESULTS

After screening 800 participants, 270 (151 men, 119 women) were selected for a study with TBM scores and cognition-related assessments at 6, 12, and 24 months. Groups included healthy controls (n=53), MCI (n=158), and Alzheimer's Disease (AD) (n=59). TBM indices correlated with cognitive scores in MCI and AD groups but not healthy controls. Changes in TBM indices and cognitive scores were significantly correlated in MCI and AD groups over 24 months. TBM indices were weak predictors of cognitive decline at all time points.

CONCLUSIONS

TBM can help physicians diagnose MCI and AD early. However, TBM could not strongly predict cognitive functions decline at all time points.

Medial prefrontal neuroplasticity during extended-release naltrexone treatment of opioid use disorder - a longitudinal structural magnetic resonance imaging study

Opioid use disorder (OUD) has been linked to macroscopic structural alterations in the brain. The monthly injectable, extended-release formulation of μ-opioid antagonist naltrexone (XR-NTX) is highly effective in reducing opioid craving and preventing opioid relapse. Here, we investigated the neuroanatomical effects of XR-NTX by examining changes in cortical thickness during treatment for OUD. Forty-seven OUD patients underwent structural magnetic resonance imaging and subjectively rated their opioid craving ≤1 day before (pre-treatment) and 11 ± 3 days after (on-treatment) the first XR-NTX injection. A sample of fifty-six non-OUD individuals completed a single imaging session and served as the comparison group. A publicly available [¹¹C]carfentanil positron emission tomography dataset was used to assess the relationship between changes in cortical thickness and μ-opioid receptor (MOR) binding potential across brain regions. We found that the thickness of the medial prefrontal and anterior cingulate cortices (mPFC/aCC; regions with high MOR binding potential) was comparable between the non-OUD individuals and the OUD patients at pre-treatment. However, among the OUD patients, mPFC/aCC thickness significantly decreased from pre-treatment to on-treatment. A greater reduction in mPFC/aCC thickness was associated with a greater reduction in opioid craving. Taken together, our study suggests XR-NTX-induced cortical thickness reduction in the mPFC/aCC regions in OUD patients. The reduction in thickness does not appear to indicate a restoration to the non-OUD level but rather reflects XR-NTX's distinct therapeutic impact on an MOR-rich brain structure. Our findings highlight the neuroplastic effects of XR-NTX that may inform the development of novel OUD interventions.

Perspectives on the Molecular Mediators of Oxidative Stress and Antioxidant Strategies in the Context of Neuroprotection and Neurolongevity: An Extensive Review

Molecules with at least one unpaired electron in their outermost shell are known as free radicals. Free radical molecules are produced either within our bodies or by external sources such as ozone, cigarette smoking, X-rays, industrial chemicals, and air pollution. Disruption of normal cellular homeostasis by redox signaling may result in cardiovascular, neurodegenerative diseases and cancer. Although ROS (reactive oxygen species) are formed in the GI tract, little is known about how they contribute to pathophysiology and disease etiology. When reactive oxygen species and antioxidants are in imbalance in our bodies, they can cause cell structure damage, neurodegenerative diseases, diabetes, hypercholesterolemia, atherosclerosis, cancer, cardiovascular diseases, metabolic disorders, and other obesity-related disorders, as well as protein misfolding, mitochondrial dysfunction, glial cell activation, and subsequent cellular apoptosis. Neuron cells are gradually destroyed in neurodegenerative diseases. The production of inappropriately aggregated proteins is strongly linked to oxidative stress. This review's goal is to provide as much information as possible about the numerous neurodegenerative illnesses linked to oxidative stress. The possibilities of multimodal and neuroprotective therapy in human illness, using already accessible medications and demonstrating neuroprotective promise in animal models, are highlighted. Neuroprotection and neurolongevity may improve from the use of bioactive substances from medicinal herbs like Allium stadium, Celastrus paniculatus, and Centella asiatica. Many neuroprotective drugs' possible role has been addressed. Preventing neuroinflammation has been demonstrated in several animal models.

Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD

Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data-sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network-based applications.

Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia

Frontotemporal dementia associated with granulin (GRN) mutations presents asymmetric brain atrophy. We applied a Minimum Spanning Tree plus an Efficiency Cost Optimization approach to cortical thickness data in order to test whether graph theory measures could identify global or local impairment of connectivity in the presymptomatic phase of pathology, where other techniques failed in demonstrating changes. We included 52 symptomatic GRN mutation carriers (SC), 161 presymptomatic GRN mutation carriers (PSC) and 341 non-carriers relatives from the Genetic Frontotemporal dementia research Initiative cohort. Group differences of global, nodal and edge connectivity in (Minimum Spanning Tree plus an Efficiency Cost Optimization) graph were tested via Structural Equation Models. Global graph perturbation was selectively impaired in SC compared to non-carriers, with no changes in PSC. At the local level, only SC exhibited perturbation of frontotemporal nodes, but edge connectivity revealed a characteristic pattern of interhemispheric disconnection, involving homologous parietal regions, in PSC. Our results suggest that GRN-related frontotemporal dementia resembles a disconnection syndrome, with interhemispheric disconnection between parietal regions in presymptomatic phases that progresses to frontotemporal areas as symptoms emerge.

Integrated structural and functional atlases of Asian children from infancy to childhood

The developing brain grows exponentially in the first few years of life. There is a need to have age-appropriate brain atlases that coherently characterize the geometry of the cerebral cortex, white matter tracts, and functional organization. This study employed multi-modal brain images of an Asian cohort and constructed brain structural and functional atlases for 6-month-old infants, 4.5-, 6-, and 7.5-year-old children. We exploited large deformation diffeomorphic metric mapping and probabilistic atlas generation approaches to integrate structural MRI and diffusion weighted images (DWIs) and to create the atlas where white matter tracts well fit into the cortical folding pattern. Based on this structural atlas, we then employed spectral clustering to parcellate the brain into functional networks from resting-state fMRI (rs-fMRI). Our results provided the atlas that characterizes the cortical folding geometry, subcortical regions, deep white matter tracts, as well as functional networks in a stereotaxic coordinate space for the four different age groups. The functional networks consisting of the primary cortex were well established in infancy and remained stable to childhood, while specific higher-order functional networks showed specific patterns of hemispherical, subcortical-cerebellar, and cortical-cortical integration and segregation from infancy to childhood. Our multi-modal fusion analysis demonstrated the use of the integrated structural and functional atlas for understanding coherent patterns of brain anatomical and functional development during childhood. Hence, our atlases can be potentially used to study coherent patterns of brain anatomical and functional development.

Cortical Structural Connectivity Alterations and Potential Pathogenesis in Mid-Stage Sporadic Parkinson's Disease

Many clinical symptoms of sporadic Parkinson's disease (sPD) cannot be completely explained by a lesion of the simple typical extrapyramidal circuit between the striatum and substantia nigra. Therefore, this study aimed to explore the new potential damaged pathogenesis of other brain regions associated with the multiple and complex clinical symptoms of sPD through magnetic resonance imaging (MRI). A total of 65 patients with mid-stage sPD and 35 healthy controls were recruited in this study. Cortical structural connectivity was assessed by seed-based analysis using the vertex-based morphology of MRI. Seven different clusters in the brain regions of cortical thickness thinning derived from the regression analysis using brain size as covariates between sPD and control were selected as seeds. Results showed that the significant alteration of cortical structural connectivity mainly occurred in the bilateral frontal orbital, opercular, triangular, precentral, rectus, supplementary-motor, temporal pole, angular, Heschl, parietal, supramarginal, postcentral, precuneus, occipital, lingual, cuneus, Rolandic-opercular, cingulum, parahippocampal, calcarine, olfactory, insula, paracentral-lobule, and fusiform regions at the mid-stage of sPD. These findings suggested that the extensive alteration of cortical structural connectivity is one of possible pathogenesis resulting in the multiple and complex clinical symptoms in sPD.

Ex-vivo Alzheimer's disease brain tissue investigation: a multiscale approach using 1060-nm swept source optical coherence tomography for a direct correlation to histology

Significance: Amyloid-beta ( A - β ) plaques are pathological protein deposits formed in the brain of Alzheimer's disease (AD) patients upon disease progression. Further research is needed to elucidate the complex underlying mechanisms involved in their formation using label-free, tissue preserving, and volumetric techniques. Aim: The aim is to achieve a one-to-one correlation of optical coherence tomography (OCT) data to histological micrographs of brain tissue using 1060-nm swept source OCT. Approach: A - β plaques were investigated in ex-vivo AD brain tissue using OCT with the capability of switching between two magnifications. For the exact correlation to histology, a 3D-printed tool was designed to generate samples with parallel flat surfaces. Large field-of-view (FoV) and sequentially high-resolution volumes at different locations were acquired. The large FoV served to align the OCT to histology images; the high-resolution images were used to visualize fine details. Results: The instrument and the presented method enabled an accurate correlation of histological micrographs with OCT data. A - β plaques were identified as hyperscattering features in both FoV OCT modalities. The plaques identified in volumetric OCT data were in good agreement with immunohistochemically derived micrographs. Conclusion: OCT combined with the 3D-printed tool is a promising approach for label-free, nondestructive, volumetric, and fast tissue analysis.

Voxel-wise meta-analysis of structural changes in gray matter of Parkinson's disease patients with mild cognitive impairment

Evidence from previous voxel-based morphometry (VBM) studies indicates that widespread brain regions are involved in Parkinson’s disease with mild cognitive impairment (PD-MCI). However, the spatial localization reported for gray matter (GM) abnormalities is heterogeneous. The aim of the present study was to quantitatively integrate studies on GM abnormalities observed in PD-MCI in order to determine whether a pattern exists. Eligible whole-brain VBM studies were identified by a systematic search of articles in PubMed and EMBASE databases spanning from 1995 to January 1, 2019. A meta-analysis was performed to investigate regional GM abnormalities in PD-MCI. The anisotropic effect size version of seed-based d mapping (AES-SDM) meta-analysis was conducted to explore the GMV differences of PD-MCI compared with PD patients with normal cognitive function (PD-NC). A total of 12 studies comprising 243 PD-MCI patients and 326 PD-NC were included in the meta-analysis. PD-MCI patients showed a robust GM decrease in the left insula and left superior temporal gyrus. Moreover, meta-regression analysis demonstrated that age, PD duration and stage, and Unified Parkinson’s Disease Rating Scale III and Mini-Mental State Examination scores might be partly correlated with the GM abnormalities observed in PD-MCI patients. The convergent findings of this quantitative meta-analysis revealed a characteristic neuroanatomical pattern in PD-MCI. The findings provide some evidence that MCI in PD may result in the breakdown of the insula and temporal gyrus, which may serve as specific regions of interest for further investigations.

Cytoarchitectonic Mapping of MRI Detects Rapid Changes in Alzheimer's Disease

The clinical and pathological progression of Alzheimer's disease often proceeds rapidly, but little is understood about its structural characteristics over short intervals. This study evaluated the short temporal characteristics of the brain structure in Alzheimer's disease through the application of cytoarchitectonic probabilistic brain mapping to measurements of gray matter density, a technique which may provide advantages over standard volumetric MRI techniques. Gray matter density was calculated using voxel-based morphometry of T1-weighted MRI obtained from Alzheimer's disease patients and healthy controls evaluated at intervals of 0.5, 1.5, 3.5, 6.5, 9.5, 12, 18, and 24 months by the MIRIAD study. The Alzheimer's disease patients had 19.1% less gray matter at 1st MRI, and this declined 81.6% faster than in healthy controls. Atrophy in the hippocampus, amygdala, and basal forebrain distinguished the Alzheimer's disease patients. Notably, the CA2 of the hippocampus was found to have atrophied significantly within 1 month. Gray matter density measurements were reliable, with intraclass correlation coefficients exceeding 0.8. Comparative atrophy in the Alzheimer's disease group agreed with manual tracing MRI studies of Alzheimer's disease while identifying atrophy on a shorter time scale than has previously been reported. Cytoarchitectonic mapping of gray matter density is reliable and sensitive to small-scale neurodegeneration, indicating its use in the future study of Alzheimer's disease.

Cognitive Correlates of Hippocampal Atrophy and Ventricular Enlargement in Adults with or without Mild Cognitive Impairment

We analyzed structural magnetic resonance imaging data from 58 cognitively normal and 101 mild cognitive impairment subjects. We used a general linear regression model to study the association between cognitive performance with hippocampal atrophy and ventricular enlargement using the radial distance method. Bilateral hippocampal atrophy was associated with baseline and longitudinal memory performance. Left hippocampal atrophy predicted longitudinal decline in visuospatial function. The multidomain ventricular analysis did not reveal any significant predictors.

Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia

Modulation of brain state, e.g., by anesthesia, alters the correlation structure of spontaneous activity, especially in the delta band. This effect has largely been attributed to the ∼ 1    Hz slow oscillation that is characteristic of anesthesia and nonrapid eye movement (NREM) sleep. However, the effect of the slow oscillation on correlation structures and the spectral content of spontaneous activity across brain states (including NREM) has not been comprehensively examined. Further, discrepancies between activity dynamics observed with hemoglobin versus calcium (GCaMP6) imaging have not been reconciled. Lastly, whether the slow oscillation replaces functional connectivity (FC) patterns typical of the alert state, or superimposes on them, remains unclear. Here, we use wide-field calcium imaging to study spontaneous cortical activity in awake, anesthetized, and naturally sleeping mice. We find modest brain state-dependent changes in infraslow correlations but larger changes in GCaMP6 delta correlations. Principal component analysis of GCaMP6 sleep/anesthesia data in the delta band revealed that the slow oscillation is largely confined to the first three components. Removal of these components revealed a correlation structure strikingly similar to that observed during wake. These results indicate that, during NREM sleep/anesthesia, the slow oscillation superimposes onto a canonical FC architecture.

Sedentary behavior associated with reduced medial temporal lobe thickness in middle-aged and older adults

Atrophy of the medial temporal lobe (MTL) occurs with aging, resulting in impaired episodic memory. Aerobic fitness is positively correlated with total hippocampal volume, a heavily studied memory-critical region within the MTL. However, research on associations between sedentary behavior and MTL subregion integrity is limited. Here we explore associations between thickness of the MTL and its subregions (namely CA1, CA23DG, fusiform gyrus, subiculum, parahippocampal, perirhinal and entorhinal cortex,), physical activity, and sedentary behavior. We assessed 35 non-demented middle-aged and older adults (25 women, 10 men; 45-75 years) using the International Physical Activity Questionnaire for older adults, which quantifies physical activity levels in MET-equivalent units and asks about the average number of hours spent sitting per day. All participants had high resolution MRI scans performed on a Siemens Allegra 3T MRI scanner, which allows for detailed investigation of the MTL. Controlling for age, total MTL thickness correlated inversely with hours of sitting/day (r = -0.37, p = 0.03). In MTL subregion analysis, parahippocampal (r = -0.45, p = 0.007), entorhinal (r = -0.33, p = 0.05) cortical and subiculum (r = -0.36, p = .04) thicknesses correlated inversely with hours of sitting/day. No significant correlations were observed between physical activity levels and MTL thickness. Though preliminary, our results suggest that more sedentary non-demented individuals have less MTL thickness. Future studies should include longitudinal analyses and explore mechanisms, as well as the efficacy of decreasing sedentary behaviors to reverse this association.

Disease modification and Neuroprotection in neurodegenerative disorders

Background

Disease modifying therapies (DMTs) are urgently needed for neurodegenerative diseases (NDD) such as Alzheimer’s disease (AD) and many other disorders characterized by protein aggregation and neurodegeneration. Despite advances in understanding the neurobiology of NDD, there are no approved DMTs.

Discussion

Defining disease-modification is critical to drug-development programs. A DMT is an intervention that produces an enduring change in the trajectory of clinical decline of an NDD by impacting the disease processes leading to nerve cell death. A DMT is neuroprotective, and neuroprotection will result in disease modification. Disease modification can be demonstrated in clinical trials by a drug-placebo difference in clinical outcomes supported by a drug-placebo difference on biomarkers reflective of the fundamental pathophysiology of the NDD. Alternatively, disease modification can be supported by findings on a staggered start or delayed withdrawal clinical trial design. Collecting multiple biomarkers is necessary to support a comprehensive view of disease modification.

Conclusion

Disease modification is established by demonstrating an enduring change in the clinical trajectory of an NDD based on intervention in the fundamental pathophysiology of the disease leading to nerve cell death. Supporting data are collected in clinical trials. Effectively defining a DMT will assist in NDD drug development programs.

Higher homocysteine associated with thinner cortical gray matter in 803 participants from the Alzheimer's Disease Neuroimaging Initiative

A significant portion of our risk for dementia in old age is associated with lifestyle factors (diet, exercise, and cardiovascular health) that are modifiable, at least in principle. One such risk factor, high-homocysteine levels in the blood, is known to increase risk for Alzheimer's disease and vascular disorders. Here, we set out to understand how homocysteine levels relate to 3D surface-based maps of cortical gray matter distribution (thickness, volume, and surface area) computed from brain magnetic resonance imaging in 803 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative data set. Individuals with higher plasma levels of homocysteine had lower gray matter thickness in bilateral frontal, parietal, occipital, and right temporal regions and lower gray matter volumes in left frontal, parietal, temporal, and occipital regions, after controlling for diagnosis, age, and sex and after correcting for multiple comparisons. No significant within-group associations were found in cognitively healthy people, patients with mild cognitive impairment, or patients with Alzheimer's disease. These regional differences in gray matter structure may be useful biomarkers to assess the effectiveness of interventions, such as vitamin B supplements, that aim to prevent homocysteine-related brain atrophy by normalizing homocysteine levels.

A review of the use of magnetic resonance imaging in Parkinson's disease

To date, the most frequently used Parkinson's disease (PD) biomarkers are the brain imaging measures of dopaminergic dysfunction using positron emission tomography and single photon emission computed tomography. However, major advances have occurred in the development of magnetic resonance imaging (MRI) biomarkers for PD in the past decade. Although conventional structural imaging remains normal in PD, advanced techniques have shown changes in the substantia nigra and the cortex. The most well-developed MRI markers in PD include diffusion imaging and iron load using T2/T2* relaxometry techniques. Other quantitative biomarkers such as susceptibility-weighted imaging for iron load, magnetization transfer and ultra-high-field MRI have shown great potential. More sophisticated techniques such as tractography and resting state functional connectivity give access to anatomical and functional connectivity changes in the brain, respectively. Brain perfusion can be assessed using non-contrast-agent techniques such as arterial spin labelling and spectroscopy gives access to metabolites concentrations. However, to date these techniques are not yet fully validated and standardized quantitative metrics for PD are still lacking. This review presents an overview of new structural, perfusion, metabolic and anatomo-functional connectivity biomarkers, their use in PD and their potential applications to improve the clinical diagnosis of Parkinsonian syndromes and the quality of clinical trials.

Mapping cortical atrophy in Parkinson's disease patients with dementia

BACKGROUND

Cognitive impairment is very common in patients with Parkinson's disease (PD). Brain changes accompanying cognitive decline in PD are still not fully established.

METHODS

We applied cortical pattern matching and cortical thickness analyses to the three-dimensional T1-weighted brain MRI scans of 14 age-matched cognitively normal elderly (NC), 12 cognitively normal PD (PDC), and 11 PD dementia (PDD) subjects. We used linear regression models to investigate the effect of diagnosis on cortical thickness. All maps were adjusted for multiple comparisons using permutation testing with a threshold p < 0.01.

RESULTS

PDD showed significantly thinner bilateral sensorimotor, perisylvian, lateral parietal, as well as right posterior cingulate, parieto-occipital, inferior temporal and lateral frontal cortices relative to NC (left p(corrected) = 0.06, right p(corrected) = 0.009). PDD showed significantly thinner bilateral sensorimotor, right frontal and right parietal-occipital cortices relative to PDC (right p(corrected) = 0.05). The absolute difference in cortical thickness between PDD and the other diagnostic groups ranged from 3% to 19%.

CONCLUSION

Our data shows that cognitive decline in PD is associated with cortical atrophy. PDD subjects have the most widespread gray matter atrophy suggesting more cortical involvement as PD patients progress to dementia.

A complex dietary supplement augments spatial learning, brain mass, and mitochondrial electron transport chain activity in aging mice

We developed a complex dietary supplement designed to offset five key mechanisms of aging and tested its effectiveness in ameliorating age-related cognitive decline using a visually cued Morris water maze test. All younger mice (<1 year old) learned the task well. However, older untreated mice (>1 year) were unable to learn the maze even after 5 days, indicative of strong cognitive decline at older ages. In contrast, no cognitive decline was evident in older supplemented mice, even when ∼2 years old. Supplemented older mice were nearly 50% better at locating the platform than age-matched controls. Brain weights of supplemented mice were significantly greater than controls, even at younger ages. Reversal of cognitive decline in activity of complexes III and IV by supplementation was significantly associated with cognitive improvement, implicating energy supply as one possible mechanism. These results represent proof of principle that complex dietary supplements can provide powerful benefits for cognitive function and brain aging.

Magnetic resonance imaging and graph theoretical analysis of complex brain networks in neuropsychiatric disorders

Neurological and psychiatric disorders disturb higher cognitive functions and are accompanied by aberrant cortico-cortical axonal pathways or synchronizations of neural activity. A large proportion of neuroimaging studies have focused on examining the focal morphological abnormalities of various gray and white matter structures or the functional activities of brain areas during goal-directed tasks or the resting state, which provides vast quantities of information on both the structural and functional alterations in the patients' brain. However, these studies often ignore the interactions among multiple brain regions that constitute complex brain networks underlying higher cognitive function. Information derived from recent advances of noninvasive magnetic resonance imaging (MRI) techniques and computational methodologies such as graph theory have allowed researchers to explore the patterns of structural and functional connectivity of healthy and diseased brains in vivo. In this article, we summarize the recent advances made in the studies of both structural (gray matter morphology and white matter fibers) and functional (synchronized neural activity) brain networks based on human MRI data pertaining to neuropsychiatric disorders. These studies bring a systems-level perspective to the alterations of the topological organization of complex brain networks and the underlying pathophysiological mechanisms. Specifically, noninvasive imaging of structural and functional brain networks and follow-up graph-theoretical analyses demonstrate the potential to establish systems-level biomarkers for clinical diagnosis, progression monitoring, and treatment effects evaluation for neuropsychiatric disorders.

Cerebellar volume in patients with dementia

OBJECTIVE

The aim of this study was to examine the cerebellar volume of subjects at different stages of Alzheimer's disease and to investigate whether volume reductions in this structure are related to cognitive decline.

METHOD

Ninety-six subjects from an epidemiological study were submitted to a magnetic resonance imaging scan and evaluated using the Mini-Mental State Examination and the Functional Activities Questionnaire. Subjects were divided into five groups according to the Clinical Dementia Rating scale. Twenty-six subjects from the original group who had no dementia diagnosis at baseline were re-evaluated for the onset of dementia after two years.

RESULTS

The volumes of the cerebellar hemispheres, posterior cerebellar lobe, vermis and temporal lobe were found to be reduced as a function of the severity of the disease. There were significant positive correlations between the volume of the temporal lobe and cerebellum and the language, attention, and total scores in the Mini-Mental State Examination and the Functional Activities Questionnaire. A logistic regression analysis demonstrated that reduced temporal lobe, posterior cerebellar lobe and vermal volume at baseline is a risk factor for the onset of dementia.

CONCLUSION

This is the first study demonstrating that reduced cerebellar volume is already apparent at the predementia stage. The results of this study support the involvement of the cerebellum in the progression of dementia. Whereas the cerebellum might not be directly associated with the origin of Alzheimer's disease, it may provide useful information related to its prognosis.

Impact of methodologic choice for automatic detection of different aspects of brain atrophy by using temporal lobe epilepsy as a model

BACKGROUND AND PURPOSE

VBM, DBM, and cortical thickness measurement techniques are commonly used automated methods to detect structural brain changes based on MR imaging. The goal of this study was to demonstrate the pathology detected by the 3 methods and to provide guidance as to which method to choose for specific research questions. This goal was accomplished by 1) identifying structural abnormalities associated with TLE with (TLE-mts) and without (TLE-no) hippocampal sclerosis, which are known to be associated with different types of brain atrophy, by using these 3 methods; and 2) determining the aspect of the disease pathology identified by each method.

MATERIALS AND METHODS

T1-weighted MR images were acquired for 15 TLE-mts patients, 14 TLE-no patients, and 33 controls on a high-field 4T scanner. Optimized VBM was carried out by using SPM software, DBM was performed by using a fluid-flow registration algorithm, and cortical thickness was analyzed by using FS-CT.

RESULTS

In TLE-mts, the most pronounced volume losses were identified in the ipsilateral hippocampus and mesial temporal region, bilateral thalamus, and cerebellum, by using SPM-VBM and DBM. In TLE-no, the most widespread changes were cortical and identified by using FS-CT, affecting the bilateral temporal lobes, insula, and frontal and occipital lobes. DBM revealed 2 clusters of reduced volume complementing FS-CT analysis. SPM-VBM did not show any significant volume losses in TLE-no.

CONCLUSIONS

These results demonstrate that the 3 methods detect different aspects of brain atrophy and that the choice of the method should be guided by the suspected pathology of the disease.

Hippocampal and ventricular changes in Parkinson's disease mild cognitive impairment

We analyzed T1-weighted brain magnetic resonance imaging data of 100 cognitively normal elderly controls (NC), 127 cognitively normal Parkinson's disease (PD; PDCN) and 31 PD-associated mild cognitive impairment (PDMCI) subjects from the Norwegian ParkWest study. Using automated segmentation methods, followed by the radial distance technique and multiple linear regression we studied the effect of clinical diagnosis on hippocampal and ventricular radial distance while adjusting for age, education, and scanning site. PDCN subjects had significantly smaller bilateral hippocampal radial distance relative to NC. Nonamnestic PDMCI subjects showed smaller right hippocampal radial distance relative to NC. PDMCI subjects showed significant enlargement of all portions of the lateral ventricles relative to NC and significantly larger bilateral temporal and occipital and left frontal lateral ventricular expansion relative to PDCN subjects. Nonamnestic PDMCI subjects showed significant ventricular enlargement spanning all parts of the lateral ventricle while those with amnestic PDMCI showed changes localized to the left occipital horn. Hippocampal atrophy and lateral ventricular enlargement show promise as structural biomarkers for PD.

Neuroinflammation resulting from covert brain invasion by common viruses - a potential role in local and global neurodegeneration

Neurodegenerative diseases are a horrendous burden for their victims, their families, and society as a whole. For half a century scientists have pursued the hypothesis that these diseases involve a chronic viral infection in the brain. However, efforts to consistently detect a specific virus in brains of patients with such diseases as Alzheimer's or multiple sclerosis have generally failed. Neuropathologists have become increasingly aware that most patients with neurodegenerative diseases demonstrate marked deterioration of the brain olfactory bulb in addition to brain targets that define the specific disease. In fact, the loss of the sense of smell may precede overt neurological symptoms by many years. This realization that the olfactory bulb is a common target in neurodegenerative diseases suggests the possibility that microbes and/or toxins in inhaled air may play a role in their pathogenesis. With regard to inhaled viruses, neuropathologists have focused on those viruses that infect and kill neurons. However, a recent study shows that a respiratory virus with no neurotropic properties can rapidly invade the mouse olfactory bulb from the nasal cavity. Available data suggest that this strain of influenza is passively transported to the bulb via the olfactory nerves (mechanism unknown), and is taken up by glial cells in the outer layers of the bulb. The infected glial cells appear to be activated by the virus, secrete proinflammatory cytokines, and block further spread of virus within the brain. At the time that influenza symptoms become apparent (15 h post-infection), but not prior to symptom onset (10 h post-infection), proinflammatory cytokine-expressing neurons are increased in olfactory cortical pathways and hypothalamus as well as in the olfactory bulb. The mice go on to die of pneumonitis with severe acute phase and respiratory disease symptoms but no classical neurological symptoms. While much remains to be learned about this intranasal influenza-brain invasion model, it suggests the hypothesis that common viruses encountered in our daily life may initiate neuroinflammation via olfactory neural networks. The numerous viruses that we inhale during a lifetime might cause the death of only a few neurons per infection, but this minor damage would accumulate over time and contribute to age-related brain shrinkage and/or neurodegenerative diseases. Elderly individuals with a strong innate inflammatory system, or ongoing systemic inflammation (or both), might be most susceptible to these outcomes. The evidence for the hypothesis that common respiratory viruses may contribute to neurodegenerative processes is developed in the accompanying article.

Manifold modeling for brain population analysis

This paper describes a method for building efficient representations of large sets of brain images. Our hypothesis is that the space spanned by a set of brain images can be captured, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The manifold model is generative-brain images can be constructed from a relatively small set of parameters, and new brain images can be projected onto the manifold. This allows to quantify the geometric accuracy of the manifold approximation in terms of projection distance. The manifold coordinates induce a Euclidean coordinate system on the population data that can be used to perform statistical analysis of the population. We evaluate the proposed method on the OASIS and ADNI brain databases of head MR images in two ways. First, the geometric fit of the method is qualitatively and quantitatively evaluated. Second, the ability of the brain manifold model to explain clinical measures is analyzed by linear regression in the manifold coordinate space. The regression models show that the manifold model is a statistically significant descriptor of clinical parameters.

Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

We used a new method we developed for automated hippocampal segmentation, called the auto context model, to analyze brain MRI scans of 400 subjects from the Alzheimer's disease neuroimaging initiative. After training the classifier on 21 hand-labeled expert segmentations, we created binary maps of the hippocampus for three age- and sex-matched groups: 100 subjects with Alzheimer's disease (AD), 200 with mild cognitive impairment (MCI) and 100 elderly controls (mean age: 75.84; SD: 6.64). Hippocampal traces were converted to parametric surface meshes and a radial atrophy mapping technique was used to compute average surface models and local statistics of atrophy. Surface-based statistical maps visualized links between regional atrophy and diagnosis (MCI versus controls: P = 0.008; MCI versus AD: P = 0.001), mini-mental state exam (MMSE) scores, and global and sum-of-boxes clinical dementia rating scores (CDR; all P < 0.0001, corrected). Right but not left hippocampal atrophy was associated with geriatric depression scores (P = 0.004, corrected); hippocampal atrophy was not associated with subsequent decline in MMSE and CDR scores, educational level, ApoE genotype, systolic or diastolic blood pressure measures, or homocysteine. We gradually reduced sample sizes and used false discovery rate curves to examine the method's power to detect associations with diagnosis and cognition in smaller samples. Forty subjects were sufficient to discriminate AD from normal and correlate atrophy with CDR scores; 104, 200, and 304 subjects, respectively, were required to correlate MMSE with atrophy, to distinguish MCI from normal, and MCI from AD.

Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution

The brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and, consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years. On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project. On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable, thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it changes over a lifetime and in the course of evolution.

Surface feature-guided mapping of cerebral metabolic changes in cognitively normal and mildly impaired elderly

PURPOSE

The aim of this study was to investigate the longitudinal positron emission tomography (PET) metabolic changes in the elderly.

PROCEDURES

Nineteen nondemented subjects (mean Mini-Mental Status Examination 29.4 +/- 0.7 SD) underwent two detailed neuropsychological evaluations and resting 2-deoxy-2-[F-18]fluoro-D: -glucose (FDG)-PET scan (interval 21.7 +/- 3.7 months), baseline structural 3T magnetic resonance (MR) imaging, and apolipoprotein E4 genotyping. Cortical PET metabolic changes were analyzed in 3-D using the cortical pattern matching technique.

RESULTS

Baseline vs. follow-up whole-group comparison revealed significant metabolic decline bilaterally in the posterior temporal, parietal, and occipital lobes and the left lateral frontal cortex. The declining group demonstrated 10-15% decline in bilateral posterior cingulate/precuneus, posterior temporal, parietal, and occipital cortices. The cognitively stable group showed 2.5-5% similarly distributed decline. ApoE4-positive individuals underwent 5-15% metabolic decline in the posterior association cortices.

CONCLUSIONS

Using 3-D surface-based MR-guided FDG-PET mapping, significant metabolic changes were seen in five posterior and the left lateral frontal regions. The changes were more pronounced for the declining relative to the cognitively stable group.

Potential adverse effects of amphetamine treatment on brain and behavior: a review

Amphetamine stimulants have been used medically since early in the twentieth century, but they have a high abuse potential and can be neurotoxic. Although they have long been used effectively to treat attention deficit hyperactivity disorder (ADHD) in children and adolescents, amphetamines are now being prescribed increasingly as maintenance therapy for ADHD and narcolepsy in adults, considerably extending the period of potential exposure. Effects of prolonged stimulant treatment have not been fully explored, and understanding such effects is a research priority. Because the pharmacokinetics of amphetamines differ between children and adults, reevaluation of the potential for adverse effects of chronic treatment of adults is essential. Despite information on the effects of stimulants in laboratory animals, profound species differences in susceptibility to stimulant-induced neurotoxicity underscore the need for systematic studies of prolonged human exposure. Early amphetamine treatment has been linked to slowing in height and weight growth in some children. Because the number of prescriptions for amphetamines has increased several fold over the past decade, an amphetamine-containing formulation is the most commonly prescribed stimulant in North America, and it is noteworthy that amphetamines are also the most abused prescription medications. Although early treatment does not increase risk for substance abuse, few studies have tracked the compliance and usage profiles of individuals who began amphetamine treatment as adults. Overall, there is concern about risk for slowed growth in young patients who are dosed continuously, and for substance abuse in patients first medicated in late adolescence or adulthood. Although most adult patients also use amphetamines effectively and safely, occasional case reports indicate that prescription use can produce marked psychological adverse events, including stimulant-induced psychosis. Assessments of central toxicity and adverse psychological effects during late adulthood and senescence of adults who receive prolonged courses of amphetamine treatment are warranted. Finally, identification of the biological factors that confer risk and those that offer protection is also needed to better specify the parameters of safe, long-term, therapeutic administration of amphetamines to adults.

MKL for robust multi-modality AD classification

We study the problem of classifying mild Alzheimer's disease (AD) subjects from healthy individuals (controls) using multi-modal image data, to facilitate early identification of AD related pathologies. Several recent papers have demonstrated that such classification is possible with MR or PET images, using machine learning methods such as SVM and boosting. These algorithms learn the classifier using one type of image data. However, AD is not well characterized by one imaging modality alone, and analysis is typically performed using several image types--each measuring a different type of structural/functional characteristic. This paper explores the AD classification problem using multiple modalities simultaneously. The difficulty here is to assess the relevance of each modality (which cannot be assumed a priori), as well as to optimize the classifier. To tackle this problem, we utilize and adapt a recently developed idea called Multi-Kernel learning (MKL). Briefly, each imaging modality spawns one (or more kernels) and we simultaneously solve for the kernel weights and a maximum margin classifier. To make the model robust, we propose strategies to suppress the influence of a small subset of outliers on the classifier--this yields an alternative minimization based algorithm for robust MKL. We present promising multi-modal classification experiments on a large dataset of images from the ADNI project.

Abuse of amphetamines and structural abnormalities in the brain

We review evidence that structural brain abnormalities are associated with abuse of amphetamines. A brief history of amphetamine use/abuse and evidence for toxicity is followed by a summary of findings from structural magnetic resonance imaging (MRI) studies of human subjects who had abused amphetamines and children who were exposed to amphetamines in utero. Evidence comes from studies that used a variety of techniques including manual tracing, pattern matching, voxel-based, tensor-based, or cortical thickness mapping, quantification of white matter signal hyperintensities, and diffusion tensor imaging. Ten studies compared controls to individuals who were exposed to methamphetamine. Three studies assessed individuals exposed to 3-4-methylenedioxymethamphetamine (MDMA). Brain structural abnormalities were consistently reported in amphetamine abusers, as compared to control subjects. These included lower cortical gray matter volume and higher striatal volume than control subjects. These differences might reflect brain features that could predispose to substance dependence. High striatal volumes might also reflect compensation for toxicity in the dopamine-rich basal ganglia. Prenatal exposure was associated with striatal volume that was below control values, suggesting that such compensation might not occur in utero. Several forms of white matter abnormality are also common and may involve gliosis. Many of the limitations and inconsistencies in the literature relate to techniques and cross-sectional designs, which cannot infer causality. Potential confounding influences include effects of pre existing risk/protective factors, development, gender, severity of amphetamine abuse, abuse of other drugs, abstinence, and differences in lifestyle. Longitudinal designs in which multimodal datasets are acquired and are subjected to multivariate analyses would enhance our ability to provide general conclusions regarding the associations between amphetamine abuse and brain structure.

Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

As one of the earliest structures to degenerate in Alzheimer's disease (AD), the hippocampus is the target of many studies of factors that influence rates of brain degeneration in the elderly. In one of the largest brain mapping studies to date, we mapped the 3D profile of hippocampal degeneration over time in 490 subjects scanned twice with brain MRI over a 1-year interval (980 scans). We examined baseline and 1-year follow-up scans of 97 AD subjects (49 males/48 females), 148 healthy control subjects (75 males/73 females), and 245 subjects with mild cognitive impairment (MCI; 160 males/85 females). We used our previously validated automated segmentation method, based on AdaBoost, to create 3D hippocampal surface models in all 980 scans. Hippocampal volume loss rates increased with worsening diagnosis (normal=0.66%/year; MCI=3.12%/year; AD=5.59%/year), and correlated with both baseline and interval changes in Mini-Mental State Examination (MMSE) scores and global and sum-of-boxes Clinical Dementia Rating scale (CDR) scores. Surface-based statistical maps visualized a selective profile of ongoing atrophy in all three diagnostic groups. Healthy controls carrying the ApoE4 gene atrophied faster than non-carriers, while more educated controls atrophied more slowly; converters from MCI to AD showed faster atrophy than non-converters. Hippocampal loss rates can be rapidly mapped, and they track cognitive decline closely enough to be used as surrogate markers of Alzheimer's disease in drug trials. They also reveal genetically greater atrophy in cognitively intact subjects.

Subregional hippocampal atrophy predicts Alzheimer's dementia in the cognitively normal

Atrophic changes of the hippocampus are typically regarded as an early sign of Alzheimer's dementia (AD). Using the radial distance atrophy mapping approach, we compared the longitudinal MRI data of 10 cognitively normal elderly subjects who remained normal at 3-year and 6-year follow-up (NL-NL) and 7 cognitively normal elderly subjects who were diagnosed with mild cognitive impairment (MCI) 2.8 (range 2.0-3.9) and with AD 6.8 years (range 6.1-8.2) after baseline (NL-MCI(AD)). 3D statistical maps revealed greater hippocampal atrophy in the NL-MCI(AD) relative to the NL-NL group at baseline (left p=0.05; right p=0.06) corresponding to 10-15% CA1, and 10-25% subicular atrophy, and bilateral differences at 3-year follow-up (left p=0.001, right p<0.02) corresponding to 10-30% subicular, 10-20% CA1, and 10-20% newly developed CA2-3 atrophy. This preliminary study suggests that excess CA1 and subicular atrophy is present in cognitively normal individuals predestined to decline to amnestic MCI, while progressive involvement of the CA1 and subiculum, and atrophy spreading to the CA2-3 subfield in amnestic MCI, suggests future diagnosis of AD.

Computational anatomical methods as applied to ageing and dementia

The cellular hallmarks of Alzheimer's disease (AD) accumulate in the living brain up to 30 years before the characteristic symptoms of dementia can be identified. Brain changes in AD are difficult to distinguish from those in normal ageing, and this has led to the development of powerful computational methods to extract statistical information on the brain changes that are characteristic of AD, mild cognitive impairment (MCI) and different dementia subtypes. Time-lapse maps can be built to show how the disease spreads in the brain, and where treatment affects the disease trajectory. Here, we review three computational approaches to map brain deficits in AD: cortical thickness maps, tensor-based morphometry and hippocampal/ventricular surface modelling. Anatomical structures, modelled as three-dimensional geometrical surfaces, are mathematically combined across subjects for group or interval comparisons. Mathematical concepts from computational surface modelling, fluid mechanics and multivariate statistics are exploited to distinguish disease from normal variations in brain structure. These methods yield insight into the dynamics of AD and MCI, showing where brain changes correlate with cognitive or behavioural changes such as language dysfunction or apathy. We describe cortical and hippocampal changes that distinguish dementia subtypes (such as Lewy-body dementia, HIV-associated dementia and AD), and we describe brain changes that predict recovery or decline in those at risk. Finally, we indicate which computational methods are powerful enough to track dementia in clinical trials, on the basis of their efficiency and sensitivity to early change, and the detail in the measures they provide.

3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

Voxel-based morphometry of temporal lobe epilepsy: an introduction and review of the literature

We review the applications and results of voxel-based morphometry (VBM) studies that have reported brain changes associated with temporal lobe epilepsy (TLE). A PubMed search yielded 18 applications of VBM to study brain abnormalities in patients with TLE up to May 2007. Across studies, 26 brain regions were found to be significantly reduced in volume relative to healthy controls. There was a strong asymmetrical distribution of temporal lobe abnormalities preferentially observed ipsilateral to the seizure focus, particularly of the hippocampus (82.35% of all studies), parahippocampal gyrus (47.06%), and entorhinal (23.52%) cortex. The contralateral hippocampus was reported as abnormal in 17.65% of studies. There was a much more bilateral distribution of extratemporal lobe atrophy, preferentially affecting the thalamus (ipsilateral = 61.11%, contralateral = 50%) and parietal lobe (ipsilateral = 47.06%, contralateral = 52.94%). VBM generally reveals a distribution of brain abnormalities in patients with TLE consistent with the region-of-interest neuroimaging and postmortem literature. It is unlikely that VBM has any clinical utility given the lack of robustness for individual comparisons. However, VBM may help elucidate some unresolved important research questions such as how recurrent temporal lobe seizures affect hippocampal and extrahippocampal morphology using serial imaging acquisitions.

Three-dimensional gray matter atrophy mapping in mild cognitive impairment and mild Alzheimer disease

BACKGROUND

Alzheimer disease (AD) is the most common form of dementia worldwide. Mild cognitive impairment (MCI) is the recent terminology for patients with cognitive deficiencies in the absence of functional decline. Most patients with MCI harbor the pathologic changes of AD and demonstrate transition to dementia at a rate of 10% to 15% per year. Patients with AD and MCI experience progressive brain atrophy.

OBJECTIVE

To analyze the structural magnetic resonance imaging data for 24 patients with amnestic MCI and 25 patients with mild AD using an advanced 3-dimensional cortical mapping technique.

DESIGN

Cross-sectional cohort design. Patients/

METHODS

We analyzed the structural magnetic resonance imaging data of 24 amnestic MCI (mean MMSE, 28.1; SD, 1.7) and 25 mild AD patients (all MMSE scores, >18; mean MMSE, 23.7; SD, 2.9) using an advanced 3-dimensional cortical mapping technique.

RESULTS

We observed significantly greater cortical atrophy in patients with mild AD. The entorhinal cortex, right more than left lateral temporal cortex, right parietal cortex, and bilateral precuneus showed 15% more atrophy and the remainder of the cortex primarily exhibited 10% to 15% more atrophy in patients with mild AD than in patients with amnestic MCI.

CONCLUSION

There are striking cortical differences between mild AD and the immediately preceding cognitive state of amnestic MCI. Cortical areas affected earlier in the disease process are more severely affected than those that are affected late. Our method may prove to be a reliable in vivo disease-tracking technique that can also be used for evaluating disease-modifying therapies in the future.